Data Governance Specialist

Role Purpose & Context

Role Summary

The Data Governance Specialist is responsible for managing the data catalogue and quality rules for a specific business domain, like Marketing or Finance. You'll spend your days making sure our data is accurate, consistent, and easy for everyone to find and understand, which directly impacts how quickly and confidently our teams can make decisions. You'll work at the intersection of our technical data platforms and the business teams who actually use the data, translating technical definitions into plain English and making sure everyone's on the same page. When this role is done well, our business users trust the numbers, find what they need quickly, and don't waste time arguing over definitions. When it's not, well, people start building their own dodgy spreadsheets, and we end up with conflicting reports and bad decisions. The challenge is often getting busy people to care about data definitions and quality when they've got other priorities. The reward, though, is seeing your work directly improve how the company operates, making everyone's life a bit easier and our decisions much smarter.

Reporting Structure

• Reports to: Senior Data Strategist
• Direct reports:
• Matrix relationships: Mid-Level Data Steward, Data Quality Analyst, Information Governance Officer,

Key Stakeholders

Internal:

• Data Engineering Team
• Analytics & BI Teams
• Marketing Operations
• Finance Business Partners
• Legal & Compliance

External:

• Data Governance software vendors (e.g., Collibra support)

Organisational Impact

Scope: This role directly improves the reliability and discoverability of our data assets. By ensuring data quality and clear definitions, you'll reduce the time people spend searching for data, fixing errors, or debating what a metric actually means. This means faster, more confident decision-making across the business, especially for teams relying on accurate customer or financial data. Essentially, you're building the trust in our data that everything else relies on.

Performance Metrics

Quantitative Metrics

1. Metric: Data Quality Score Improvement
2. Desc: Improving the accuracy and completeness of critical data elements (CDEs) within your assigned business domain.
3. Target: Increase average data quality score from 80% to 95% for 3-5 key CDEs within 6 months.
4. Freq: Monthly via Collibra Data Quality dashboards.
5. Example: If 'Customer Email Address' quality is 82% today, your work should get it to 95% by month six, meaning fewer invalid emails in our CRM.
6. Metric: Data Catalogue Enrichment
7. Desc: The number of new business terms, data assets, and data lineage maps added and validated in our data catalogue.
8. Target: Add and validate 150+ new business terms and 20+ data lineage maps in Collibra per quarter.
9. Freq: Quarterly review of Collibra audit logs and content creation reports.
10. Example: Documenting all the fields in our 'Marketing Campaign' table, defining 'Campaign ROI' in plain English, and mapping its journey from Salesforce to Tableau.
11. Metric: SLA Adherence for Data Requests
12. Desc: Resolving data access requests, definition queries, and minor data quality issues within agreed service level agreements.
13. Target: Resolve 98% of data catalogue and access requests within the 48-hour business SLA.
14. Freq: Weekly review of ticketing system (e.g., Jira Service Desk) reports.
15. Example: A Marketing Analyst asks for access to a new dataset; you get them set up and the access confirmed within 24 hours.
16. Metric: Reduction in Data Definition Discrepancies
17. Desc: Decreasing instances where different teams have conflicting definitions for the same business metric or data element.
18. Target: Reduce identified definition discrepancies by 30% in your domain within 9 months.
19. Freq: Quarterly audit of key reports and stakeholder feedback.
20. Example: Getting Sales and Finance to agree on a single, documented definition for 'Active Customer' in Collibra, rather than each using their own.

Qualitative Metrics

1. Metric: Stakeholder Engagement & Collaboration
2. Desc: How effectively you work with business teams to understand their data needs and get their buy-in on governance policies.
3. Evidence: Business users proactively come to you with data questions or issues. You're regularly invited to departmental meetings to discuss data. Feedback from colleagues mentions your helpfulness and clarity in communication. You're seen as a go-to person for data definitions and understanding.
4. Metric: Proactive Problem Identification
5. Desc: Your ability to spot potential data quality issues or governance gaps before they become major problems.
6. Evidence: You flag an inconsistency in a data source before it impacts a critical dashboard. You propose a new data quality rule based on observing user behaviour. You identify a missing piece of data lineage and proactively map it out. You don't just react; you anticipate.
7. Metric: Documentation Clarity & Completeness
8. Desc: The quality and user-friendliness of the data definitions, policies, and lineage you create and maintain.
9. Evidence: New joiners can easily understand data definitions from the catalogue. Business users can find the data they need without asking for help. Audit trails in Collibra show consistent updates and adherence to standards. Your documentation is clear, concise, and actually used.
10. Metric: Adherence to Governance Standards
11. Desc: How well you follow and help enforce the established data governance policies and procedures.
12. Evidence: All new data assets in your domain are properly onboarded into the catalogue. Data quality rules are consistently applied and monitored. You correctly escalate policy breaches when they occur. You're a champion for good data hygiene.

Primary Traits

• Trait: The Data Detective
• Manifestation: You love digging into a dataset when something just doesn't look right. You'll trace a number back through three different systems to find out why it's off by a few pence. You're the one who notices the subtle inconsistency in a report that everyone else missed. When someone says, 'the numbers don't add up,' you see it as a puzzle to solve, not a problem to ignore.
• Benefit: Our business relies on accurate data for everything from customer targeting to financial reporting. One small error can snowball into a huge problem, costing us money or damaging our reputation. You'll be on the front line, ensuring the integrity of our data. If you don't care about the details, who will?
• Trait: The Patient Translator
• Manifestation: You can explain complex technical data concepts to someone in Marketing without using jargon, and you'll do it patiently, even if you have to repeat yourself. You're good at listening to what a business user *thinks* they want and then helping them articulate what they *actually* need from the data. You don't get frustrated when people don't immediately 'get' data governance.
• Benefit: Data governance only works if people understand it and buy into it. You'll be bridging the gap between our technical data teams and the business users. If you can't translate, people won't use the catalogue, they won't follow the rules, and all our efforts will be wasted. It's about building bridges, not walls.
• Trait: The Organised Custodian
• Manifestation: Your desk is probably tidy, your files are neatly organised, and you love a good checklist. You'll make sure every new data asset is properly documented, every definition is clear, and every data quality rule is applied consistently. You're the person who ensures that when someone searches for 'Customer Lifetime Value' in our catalogue, they find one, clear, agreed-upon definition.
• Benefit: Without proper organisation, our data landscape becomes a wild west. People can't find what they need, they use outdated information, and trust erodes. You'll be the custodian of our data assets, ensuring they're well-maintained and accessible. This isn't just about tidiness; it's about enabling the entire company to work effectively with data.

Supporting Traits

• Trait: Curious
• Desc: You're always asking 'why?' and 'how does that work?' when it comes to data and systems.
• Trait: Collaborative
• Desc: You enjoy working with different teams to solve problems, rather than just working in isolation.
• Trait: Problem-Solver
• Desc: You don't just identify issues; you actively look for practical solutions, even if they're not obvious.
• Trait: Persistent
• Desc: You don't give up easily when faced with messy data or resistant stakeholders; you'll keep pushing for improvement.

Primary Motivators

1. Motivator: Bringing Order to Chaos
2. Daily: You'll get a real kick out of taking a messy, undocumented dataset and transforming it into something clean, clear, and usable. The satisfaction comes from seeing a clear definition replace ambiguity, or a data quality error being resolved.
3. Motivator: Being the Go-To Expert
4. Daily: You'll enjoy being the person people come to when they have a question about a specific piece of data, or when they need help finding reliable information. You'll build a reputation as the domain expert for data quality and definitions.
5. Motivator: Driving Tangible Improvement
6. Daily: You're motivated by seeing your work directly improve how others operate. It's not just about policies; it's about making data more accessible and trustworthy, which in turn leads to better business outcomes.

Potential Demotivators

Honestly, this role isn't for everyone. You'll spend a fair bit of time chasing people for information or trying to convince them that data quality actually matters. You might find yourself repeatedly explaining the same concept to different teams. There will be times when you identify a significant data quality issue in a source system, but the team who owns that system doesn't prioritise fixing it quickly. You might put a lot of effort into documenting something beautifully, only for it to be ignored by some users. If you need constant, immediate gratification from seeing large-scale, strategic shifts, you might find the day-to-day work a bit frustrating.

Common Frustrations

1. Dealing with 'shadow IT' – people using their own spreadsheets because they don't trust the official data.
2. Getting buy-in from busy stakeholders who see data governance as 'extra work' rather than fundamental.
3. Identifying data quality issues that originate in systems you don't control, leading to slow fixes.
4. The constant need to educate and re-educate users on data definitions and best practices.
5. The slow pace of change when trying to implement new governance policies across a large organisation.

What Role Doesn't Offer

1. A purely technical, heads-down coding role – you'll be interacting with people constantly.
2. Immediate, high-level strategic decision-making responsibility – you'll be executing strategy, not defining it.
3. A role where data is always perfectly clean and well-understood – you're here to fix that!
4. Complete autonomy over data engineering pipelines – you'll work with those teams, but you won't own their code.

ADHD Positives

1. The 'detective' aspect of finding data anomalies and solving puzzles can be highly engaging and stimulating.
2. The varied nature of tasks – from documenting to investigating to communicating – can prevent boredom.
3. The focus on clear, structured documentation (like data catalogues) can provide a helpful framework.

ADHD Challenges and Accommodations

1. The need for meticulous, consistent documentation can be challenging; using templates and automated tools (like Collibra) can help. We can also explore tools for focus and task management.
2. Repetitive tasks, like chasing stakeholders for definitions, might be less engaging; we can look at automating reminders or varying your approach.
3. We can offer flexible working hours to accommodate peak focus times and provide a quiet workspace if needed.

Dyslexia Positives

1. The strong emphasis on clear, concise, and structured documentation will benefit everyone, including those with dyslexia.
2. Tools that support visual mapping (like data lineage diagrams) can be a great way to process and present information.
3. The role's focus on logical problem-solving and pattern recognition can be a strength.

Dyslexia Challenges and Accommodations

1. Extensive reading and writing of detailed policies or definitions might be tiring; we encourage the use of text-to-speech software, grammar checkers, and templates.
2. Proofreading your own work can be harder; we'll encourage peer review for critical documentation and offer tools to assist.
3. We can provide materials in accessible formats and allow for verbal communication where written might be a barrier.

Autism Positives

1. The logical, systematic nature of data governance – defining rules, categorising data, ensuring consistency – can be very appealing.
2. The focus on detail and accuracy is a significant strength in this role.
3. Working with structured data and clear processes can provide a sense of predictability and order.

Autism Challenges and Accommodations

1. The 'patient translator' aspect involves a lot of nuanced social interaction and understanding unspoken cues; we can provide training and support for stakeholder engagement strategies.
2. Unexpected changes to data sources or business requirements might be unsettling; clear communication about changes and their impact will be crucial.
3. We can offer a consistent work environment, clear expectations for communication, and support for navigating social dynamics.

Key Responsibilities

Experience Levels Responsibilities

1. Level: Mid-Level Professional (2-5 years)
2. Responsibilities: Take ownership of the data catalogue for your assigned business domain (e.g., Marketing, Sales). This means making sure all relevant data assets, business terms, and definitions are accurately captured and kept up-to-date in Collibra.
3. Independently execute data quality checks and monitoring for critical data elements within your domain. You'll spot anomalies, investigate their root causes, and work with data engineering or source system owners to get them fixed.
4. Propose and document new data quality rules and governance policies specific to your domain. This isn't just about following rules; it's about making them better based on what you observe and learn.
5. Help business users understand and use the data catalogue effectively. You'll run small training sessions, answer questions, and generally act as the first point of contact for data definition queries in your area.
6. Identify and map data lineage for key reports and data flows within your domain, helping everyone understand where the data comes from and how it transforms.
7. Collaborate with data engineering teams to ensure new data pipelines and transformations adhere to our governance standards and that metadata is captured correctly from the start.
8. Assist in preparing reports on data quality and governance adherence for your domain, giving a clear picture of how we're doing and where we need to improve.
9. Supervision: You'll have weekly check-ins with your Senior Data Strategist, but on routine tasks, you'll be expected to work independently. For novel or complex problems, you'll escalate and get guidance. We trust you to manage your day-to-day, but we're here to support you when things get tricky.
10. Decision: You'll have authority to make routine decisions within established data governance guidelines, such as approving minor updates to data definitions or resolving common data quality issues. For anything outside the guidelines, or for significant changes to policies, you'll need to escalate to your Senior Data Strategist. You can recommend tool choices for minor tasks but won't be making large budget decisions, say anything over £5K, without approval.
11. Success: You'll know you're doing well when your assigned domain's data quality scores consistently improve, business users in that area proactively use the data catalogue, and you're seen as the reliable expert for data definitions and issues. Basically, if the data in your domain becomes noticeably cleaner and easier to understand, you're smashing it.

Decision-Making Authority

• Type: Data Definition Changes
• Entry: Propose changes to supervisor; supervisor approves.
• Mid: Approve minor updates to existing definitions within your domain. Propose significant changes to Senior Data Strategist for review and approval.
• Senior: Approve all definition changes within your workstream, consult Director on cross-domain impacts.
• Type: Data Quality Rule Implementation
• Entry: Execute rules defined by others.
• Mid: Propose and implement new data quality rules for your assigned domain, with Senior Data Strategist review for major impacts.
• Senior: Design and implement data quality frameworks across multiple domains.
• Type: Data Access Requests
• Entry: Forward requests to appropriate team; assist with basic access setup under supervision.
• Mid: Process and approve routine data access requests based on established policies. Escalate complex or non-standard requests.
• Senior: Define and manage data access policies and approval workflows.
• Type: Tool Selection (Minor)
• Entry: No authority.
• Mid: Recommend small, low-cost tools or features to improve your workflow (e.g., a new Collibra integration), but require approval for purchase.
• Senior: Evaluate and recommend new tools for specific projects or workstreams (up to £5K budget).

Competency Requirements

Foundation Skills (Transferable)

These are the core human skills that underpin everything you'll do. We're looking for someone who can not only understand data but also communicate about it, solve problems creatively, and adapt to a constantly evolving data landscape. These aren't just 'nice-to-haves'; they're essential for making a real impact.

• Category: Communication & Collaboration
• Skills: Clear and concise written communication: You can write data definitions and policies that are easy for anyone to understand, without jargon. You can draft emails that get straight to the point and get people to act.
• Active listening: You're genuinely good at hearing what business users are trying to achieve, even if they're not articulating it perfectly, and translating that into data requirements.
• Stakeholder engagement: You can build relationships with different teams (Marketing, Finance, Engineering) and get their buy-in on data governance initiatives, even when it means extra work for them.
• Facilitation skills: You can run small meetings or workshops to gather requirements for data definitions or discuss data quality issues, keeping everyone on track.
• Category: Problem-Solving & Analytical Thinking
• Skills: Root cause analysis: When a data quality issue pops up, you're able to systematically investigate and pinpoint exactly where and why it's happening, rather than just treating the symptom.
• Logical reasoning: You can think through complex data flows and identify potential inconsistencies or gaps in our governance framework.
• Critical thinking: You don't just accept data at face value; you question it, look for biases, and understand its limitations before making conclusions.
• Pattern recognition: You're good at spotting trends or unusual patterns in data that might indicate an underlying quality problem.
• Category: Adaptability & Learning Agility
• Skills: Comfort with ambiguity: The data world is rarely perfect; you're okay with incomplete information and can still make progress.
• Continuous learning: You're keen to keep up with new data governance tools, methodologies, and industry best practices.
• Flexibility: You can adjust your approach when priorities shift or when a new data source throws a curveball.
• Resilience: You can bounce back when a data project hits a snag or when a stakeholder pushes back on a policy.

Functional Skills (Role-Specific Technical)

These are the specific methodologies, tools, and technical know-how you'll need to hit the ground running. We're looking for someone who isn't just theoretically aware of these concepts but has actually applied them in a real-world setting, especially within a technical data environment.

Technical Competencies

• Skill: Data Governance Frameworks (e.g., DAMA-DMBOK principles)
• Desc: You understand the core principles of data governance – things like data quality, metadata management, data architecture, and data security. You know how these pieces fit together to create a robust data environment, even if you're not designing the whole thing.
• Level: Intermediate
• Skill: Enterprise Data Modelling Concepts
• Desc: You understand fundamental data modelling concepts like star schemas, normalisation, and the difference between logical and physical models. You don't need to be an architect, but you should be able to read and interpret data models and understand their impact on data quality and usability.
• Level: Intermediate
• Skill: Information Lifecycle Management (ILM) Basics
• Desc: You grasp the basics of how data moves through its lifecycle, from creation to archival and deletion. You understand why data retention policies matter for compliance and cost, and how to apply them.
• Level: Basic
• Skill: SQL Querying
• Desc: You can write complex SQL queries to extract, analyse, and validate data from various sources. This is essential for investigating data quality issues and understanding data lineage.
• Level: Advanced

Digital Tools

• Tool: Collibra (Data Governance & Catalog)
• Level: Advanced
• Usage: You'll be using Collibra daily to manage business terms, data assets, data lineage, and data quality rules for your assigned domain. You'll be comfortable creating new content, updating existing entries, and guiding users on how to find what they need.
• Tool: Snowflake (Data Warehouse)
• Level: Intermediate
• Usage: You'll need to execute complex SQL queries in Snowflake to investigate data quality issues, validate definitions, and understand data transformations. You won't be building pipelines, but you'll be querying the results.
• Tool: Tableau (Business Intelligence)
• Level: Intermediate
• Usage: You'll be using Tableau to review dashboards, understand how business users are consuming data, and identify potential areas for data quality or definition improvement. You might build simple validation dashboards.
• Tool: Jira Service Desk (Ticketing System)
• Level: Intermediate
• Usage: You'll manage incoming data access requests, data definition queries, and data quality incident reports through Jira, ensuring timely resolution and communication with stakeholders.
• Tool: Microsoft Excel/Google Sheets
• Level: Advanced
• Usage: For ad-hoc data analysis, reconciliation, and documenting smaller data quality investigations before formalising them in Collibra. You'll be using advanced functions and pivot tables.

Industry Knowledge

• Area: Data Privacy Regulations (e.g., GDPR, CCPA)
• Desc: You understand the basic principles of data privacy regulations and how they impact data collection, storage, and usage. You know why we need to be careful with personal data and how governance helps us stay compliant.
• Area: Technical Data Ecosystems
• Desc: You have a good grasp of how different components of a modern data stack (data warehouses, ETL tools, BI platforms) fit together, even if you're not an expert in each one. You understand the flow of data from source to consumption.

Regulatory Compliance Regulations

• Reg: General Data Protection Regulation (GDPR)
• Usage: You'll need to understand the core principles of GDPR (e.g., lawful basis, data minimisation, data subject rights) and how they apply to the data you're governing. You'll help ensure our data definitions and policies support GDPR compliance, especially for personal data.
• Reg: Local Data Protection Acts (e.g., UK Data Protection Act 2018)
• Usage: Similar to GDPR, you'll need to understand the nuances of local data protection laws that apply to our operations, ensuring our data governance practices are aligned.

Essential Prerequisites

• At least 2 years of hands-on experience in a data-focused role, such as a Data Analyst, BI Developer, or Junior Data Steward, where you regularly worked with data quality or metadata.
• Demonstrable experience with SQL for data querying and analysis in a professional setting.
• Proven ability to communicate complex data concepts clearly to non-technical audiences, both verbally and in writing.
• Experience using a data catalogue or similar metadata management tool (even if not Collibra) to document data assets.
• A solid understanding of basic data warehousing concepts and how data flows through different systems.

Career Pathway Context

This role is a fantastic step up for someone who's been working with data for a couple of years and wants to specialise in making that data truly reliable and understandable. You've probably seen the chaos of bad data and now you want to be part of the solution. It's about moving from just using data to actively shaping its quality and accessibility for others.

Qualifications & Credentials

Emerging Foundation Skills

• Skill: AI-Assisted Data Governance Tools
• Why: AI is rapidly automating many of the manual tasks in data governance, from metadata discovery to data quality anomaly detection. Specialists who can effectively use these tools will be far more productive and strategic than those who stick to manual methods.
• Concepts: [{'concept_name': 'Automated Metadata Discovery', 'description': 'Understanding how AI can scan data sources to automatically identify tables, columns, and relationships, populating the data catalogue with minimal human intervention.'}, {'concept_name': 'Anomaly Detection in Data Quality', 'description': "Learning how AI algorithms can establish 'normal' data patterns and flag deviations that indicate potential quality issues before they impact reports."}, {'concept_name': 'Natural Language Processing (NLP) for Business Glossary', 'description': 'Using AI to help suggest, refine, and link business terms in the data catalogue, making it easier to build and maintain a comprehensive glossary.'}, {'concept_name': 'Data Lineage Automation', 'description': "How AI can automatically trace data's journey through pipelines and transformations, significantly reducing the manual effort of mapping lineage."}]
• Prepare: This month: Research leading AI-powered data governance platforms (e.g., Atlan, Monte Carlo) and watch demo videos.
• Next quarter: Propose a small pilot project using an AI-assisted feature within Collibra (if available) or a trial of a new tool.
• Month 4-6: Take an online course on 'AI for Data Management' or 'Data Observability' to deepen your understanding.
• Ongoing: Actively participate in webinars and industry discussions around AI in data governance.
• QuickWin: Start experimenting with generative AI (like ChatGPT or Claude) to draft data definitions or policy summaries. It's a low-risk way to get comfortable with the technology and see immediate time savings.
• Skill: Data Product Thinking
• Why: More and more organisations are treating data as a product, with its own users, lifecycle, and quality standards. Understanding this mindset is crucial for designing governance that supports data 'products' rather than just raw datasets.
• Concepts: [{'concept_name': 'Data as a Product Principles', 'description': 'Understanding the core ideas: domain ownership, data as a product, federated governance, and a self-serve data platform.'}, {'concept_name': 'User-Centric Data Design', 'description': 'Thinking about data from the perspective of its consumers – what do they need, how do they want to access it, and what makes it valuable to them?'}, {'concept_name': 'Data Product Lifecycle', 'description': 'Managing data from its creation to deprecation, much like a software product, with versioning, quality gates, and clear documentation.'}, {'concept_name': 'Measuring Data Product Value', 'description': 'How to define and track metrics that demonstrate the business value of a data product.'}]
• Prepare: This month: Read articles and listen to podcasts on 'Data Mesh' and 'Data as a Product' concepts.
• Next quarter: Identify one key dataset in your domain and try to document it as if it were a 'product' – thinking about its users, purpose, and quality metrics.
• Month 4-6: Seek opportunities to collaborate with product managers or business analysts to understand their 'product' mindset.
• Ongoing: Look for internal examples of data being treated as a product and learn from them.
• QuickWin: When documenting a new data asset, add a 'Target Users' and 'Business Purpose' section to its description in Collibra. It's a small shift in mindset that makes a big difference.

Advancing Technical Skills

• Skill: Cloud Data Architecture Fundamentals (AWS)
• Why: Most modern data stacks are built on cloud platforms like AWS. Understanding the basic components (S3, Redshift, Glue) will help you better map data lineage, understand data storage costs, and collaborate more effectively with data engineers.
• Concepts: [{'concept_name': 'Cloud Storage (AWS S3)', 'description': 'Understanding how data lakes work and how data is stored in object storage.'}, {'concept_name': 'Cloud Data Warehousing (AWS Redshift)', 'description': 'Basic knowledge of how cloud data warehouses function and their role in analytics.'}, {'concept_name': 'ETL/ELT Concepts (AWS Glue)', 'description': 'Understanding the principles of data extraction, transformation, and loading in a cloud environment.'}, {'concept_name': 'Cloud Security Basics', 'description': 'Awareness of fundamental security concepts like IAM roles and data encryption in the cloud.'}]
• Prepare: This month: Complete a free introductory course on AWS Cloud Practitioner essentials.
• Next quarter: Shadow a Data Engineer for a day to see how they interact with AWS services.
• Month 4-6: Try to set up a very basic AWS S3 bucket and upload some dummy data to get hands-on experience.
• Ongoing: Read documentation for AWS data services relevant to our stack.
• QuickWin: Ask a Data Engineer to walk you through the architecture diagram of one of our key data pipelines, explaining each AWS component. Don't be afraid to ask 'stupid' questions!

Education Requirements

• Level: Minimum
• Req: A Bachelor's degree in a quantitative field such as Computer Science, Data Science, Information Systems, Statistics, or a related discipline.
• Alts: We're pragmatic, so if you don't have a degree, we'll consider equivalent professional experience (typically an additional 2-3 years) combined with relevant certifications that demonstrate a strong understanding of data principles.
• Level: Preferred
• Req: A Master's degree in a relevant field.
• Alts: Not essential, but it certainly shows a deeper academic grounding. Real-world experience often trumps another degree, though.

Experience Requirements

You'll need roughly 2-5 years of hands-on experience in a data-centric role. This should include significant time spent on data quality, metadata management, or working with data governance tools. We're looking for someone who has actually dealt with messy data, understood its impact, and actively worked to improve it. Experience in a technical data environment, rather than just business analysis, is key here.

Preferred Certifications

• Cert: Certified Data Management Professional (CDMP) - Associate Level
• Prod: DAMA International
• Usage: This certification validates a foundational understanding of data management principles, including data governance, data quality, and metadata management, which are directly relevant to this role.
• Cert: Collibra Ranger Certification
• Prod: Collibra
• Usage: Given our heavy reliance on Collibra, having a certification in this platform would demonstrate immediate practical proficiency and significantly reduce your onboarding time. It shows you've already got the specific skills we need.
• Cert: AWS Certified Cloud Practitioner
• Prod: Amazon Web Services (AWS)
• Usage: A basic understanding of cloud concepts and AWS services, where much of our data resides, is incredibly helpful for understanding data flows and collaborating with engineering teams.

Recommended Activities

• Regularly attending industry webinars and virtual conferences on data governance, data quality, and data management.
• Joining relevant professional communities or online forums to learn from peers and share insights.
• Reading key industry publications and blogs (e.g., Gartner, Forrester, Dataversity) to stay abreast of trends.
• Taking specialised online courses on topics like advanced SQL, data modelling, or specific data governance tools.

Career Progression Pathways

Entry Paths to This Role

• Path: Data Analyst / BI Developer
• Time: 2-3 years
• Path: Junior Data Steward / Data Quality Assistant
• Time: 1-2 years
• Path: IT Business Analyst (with Data Focus)
• Time: 3-4 years

Career Progression From This Role

• Pathway: Senior Data Strategist
• Time: 3-5 years

Long Term Vision Potential Roles

• Title: Principal Data Architect
• Time: 5-8 years from this role
• Title: Manager, Data Governance & Strategy
• Time: 4-6 years from this role
• Title: Director of Enterprise Data Strategy
• Time: 8-12 years from this role

How Zavmo Delivers This Role's Development

DISCOVER Phase: Skills Gap Analysis

Zavmo maps your current competencies against all requirements in this job description through conversational assessment. We evaluate your foundation skills (communication, strategic thinking), functional skills (CRM expertise, negotiation), and readiness for career progression.

Output: Personalised skills gap heat map showing strengths and priorities, estimated time to competency, neurodiversity accommodations.

DISCUSS Phase: Personalised Learning Pathway

Based on your DISCOVER results, Zavmo creates a personalised learning plan prioritised by impact: foundation skills first, then functional skills. We adapt to your learning style, pace, and neurodiversity needs (ADHD, dyslexia, autism).

Output: Week-by-week schedule, each module linked to specific job responsibilities, checkpoints and milestones.

DELIVER Phase: Conversational Learning

Learn through conversation, not boring modules. Zavmo uses 10 conversation types (Socratic dialogue, role-play, coaching, case studies) to build competence. Practice difficult QBR presentations, negotiate tough renewals, and handle churn conversations in a safe AI environment before facing real clients.

Example: "For 'Stakeholder Mapping', Zavmo will guide you through analysing a complex enterprise account, identifying key decision-makers, and building an engagement strategy."

DEMONSTRATE Phase: Competency Assessment

Zavmo automatically builds your evidence portfolio as you learn. Every conversation, practice scenario, and application example is captured and mapped to NOS performance criteria. When ready, your portfolio supports OFQUAL qualification claims and demonstrates competence to employers.

Output: Competency matrix, evidence portfolio (downloadable), qualification readiness, career progression score.