UK

United Kingdom · Technology and data · Director/VP (16-20 years)

Director, Analytics Engineering

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

See how AI is changing this job, and get the full read by email.

Start my free Data Fluency module

As a Director, Analytics Engineering, a good place to start on Data Mesh / Data Fabric Strategic Implementation is Using Data to Direct a Department's Priorities, a free Future Fluency module at Level 5. You'd practise using the data your department already produces to decide where its effort should go.

Free to start, no card.

Keep this job description

Where should we send it?

One email: what AI is changing in this job, the new skills it asks for, where it leads, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

Your employer, or 'self-employed'

Leading a team? Try this with up to five of them for 14 days, free.

  • Experience bandDirector/VP (16-20 years)
  • Reports toVP of Data or Chief Data Officer
  • UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as Head of Data Platform · VP, Data Architecture · Director of Data Engineering & Analytics

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 6 of 10 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 10 standards
Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 2 of 10 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 10 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Analysis and Visualisation OTHM Level 7 Diploma in Data Science
Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data-led Decision Making Level 6 Award in Data-led Decision Making
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Director, Analytics Engineering

5 units that map to this job, from the qualifications that cover it.

  1. Data Analysis and Visualisation OTHM Qualifications · covers 6 of 10 standards Level 7
  2. Introduction to Data Science and Big Data NCC Education Limited · covers 4 of 10 standards Level 5
  3. Data Analytics Pearson Education Ltd · covers 4 of 10 standards Level 5
  4. Data-led Decision Making Institute of Sales Professionals · covers 1 of 10 standards Level 6
  5. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 1 of 10 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Director, Analytics Engineering?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Data Mesh / Data Fabric Strategic Implementation
  • Ethical AI & Data Bias Mitigation
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Director, Analytics Engineering plan as you set up, starting with the free Data Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

Ready to pay? Subscribe for £70 a month

Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

A Director of Analytics Engineering plans the data systems a business unit relies on, overseeing structure, people and direction rather than individual pipelines.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

dbt Enterprise Expert

Making strategic decisions on dbt implementation, including multi-project deployments, cross-project dependencies, and managing enterprise features like the Semantic Layer and governance controls across your organisation.

Snowflake / Databricks Expert

Leading platform evaluation and selection, architecting enterprise-wide data sharing strategies, disaster recovery plans, and governing overall platform spend and architecture for your business unit. You're the ultimate decision-maker here.

Looker / Power BI Premium Expert

Governing the entire BI platform for your business unit, defining the enterprise semantic layer strategy, and managing capacity, licensing, and integration with other systems (e.g., embedding) to ensure widespread data adoption.

Git (Enterprise Strategy) Expert

Setting organisational policies for code repositories, security (e.g., secrets management with HashiCorp Vault), and artifact management across all data engineering teams. You're defining how we manage our code assets.

Airflow / Dagster / Prefect (Enterprise Architecture) Expert

Architecting the enterprise orchestration platform for your business unit, deciding on deployment patterns (e.g., KubernetesExecutor), managing cross-team dependencies, and ensuring platform scalability and resilience.

Monte Carlo / Collibra Expert

Defining the enterprise data observability and governance strategy for your business unit, selecting and implementing platforms, establishing data SLAs, and reporting on data trust to executive leadership and the Board.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Data Platform Strategy & Architecture Follows established patterns, escalates any deviation. Proposes architectural changes for specific projects, gets approval from senior engineers. Designs and implements architectural patterns for workstreams, consults Lead/Staff on major changes.
Budget Allocation & Vendor Selection No authority over budget or vendor selection. Recommends specific tools or resources for project needs, with manager approval. Recommends tools/vendors up to £5K, consults with Lead/Manager for approval.
Team Structure & Hiring No involvement in hiring or team structure decisions. Participates in technical interviews for junior roles, provides feedback. Leads technical interviews, provides strong recommendations for hiring, contributes to team skill gap analysis.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Business Unit Revenue Impact from Data Initiatives
The measurable increase in revenue or cost savings directly attributable to data products and insights developed by your teams.
Target · Identify and realise £2M+ in annualised business value (revenue uplift or cost saving) through data initiatives.

Your team's work on optimising customer acquisition funnels, using predictive models, contributes to a 10% increase in conversion, adding £2.5M to the business unit's annual revenue.

Data Platform Cost Optimisation
The efficiency of our data infrastructure spend, specifically focusing on reducing compute and storage costs while maintaining performance.
Target · Reduce data platform (e.g., Snowflake) compute costs by 15% year-on-year for your business unit.

Through strategic architecture changes and resource governance, you decrease Snowflake expenditure from £500K to £425K within 12 months, without impacting data availability.

Critical Data Asset Uptime & Freshness
The reliability and timeliness of our most important data models and dashboards, ensuring business users always have access to accurate, up-to-date information.
Target · Achieve 99.9% uptime for all P1 (critical) data pipelines and a 95% on-time delivery rate for daily data refreshes.

Our executive dashboard, which relies on 20 critical data models, is available and refreshed by 8 AM GMT every day, 99.9% of the time, meaning leadership can always trust the numbers.

Team Engagement & Retention
The overall health and stability of your Analytics Engineering organisation, reflecting your ability to attract, develop, and retain top talent.
Target · Maintain an annual voluntary attrition rate below 10% and achieve an average engagement score of 80%+ in internal surveys.

Your team's annual engagement survey results show an 85% satisfaction rate, and only 2 out of 30 team members leave voluntarily over the year, indicating a healthy and thriving team culture.

Strategic Data Vision & Roadmap
Your ability to articulate a clear, compelling, and actionable data strategy that aligns with the business unit's goals and anticipates future needs.
  • Regular positive feedback from C-Suite on data strategy presentations. The data roadmap is clearly understood and bought into by key business stakeholders. Your vision drives tangible investment and resource allocation decisions.
Organisational Leadership & Talent Development
How effectively you build, mentor, and empower your leadership team and foster a culture of technical excellence and continuous learning across your organisation.
  • Managers reporting to you demonstrate strong leadership and team performance. A clear succession plan exists for key roles. Your teams are recognised internally and externally for their technical contributions and innovation. You're seen as a trusted mentor and coach.
Cross-Functional Influence & Collaboration
Your effectiveness in building strong relationships and driving alignment with other executive leaders (e.g., Product, Engineering, Marketing, Finance) to ensure data initiatives are integrated and supported.
  • You're proactively invited to strategic planning sessions across departments. Key cross-functional projects consistently meet data-related milestones. You successfully mediate disagreements between teams regarding data definitions or ownership. People come to you for advice on data-related challenges, even outside your direct remit.
Data Governance & Trust
The establishment and enforcement of robust data governance policies, ensuring the business unit's data is accurate, secure, compliant, and trusted by all users.
  • Zero critical data incidents reported due to governance gaps. Regular audits confirm compliance with internal policies and external regulations (e.g., GDPR). Business users consistently express high confidence in the data presented in dashboards and reports. You've established clear data ownership and accountability.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Driving Business Transformation through Data

You'll spend your days in strategic meetings, designing how data can unlock new revenue streams or dramatically cut costs. You'll see your decisions directly impact the company's trajectory, not just a single project. It's about building a data-driven enterprise.

Leading the initiative to use real-time data to dynamically price products, resulting in a 5% increase in gross margin for the business unit.

Building & Empowering High-Performing Teams

A significant part of your role is about coaching managers, setting clear expectations, and removing roadblocks for your teams. You'll get satisfaction from seeing your people grow, take on bigger challenges, and deliver exceptional work. It's about creating a legacy of talent.

Mentoring an Analytics Engineering Manager who then successfully launches a critical new data product and gets promoted to a senior leadership role.

Architecting Scalable & Resilient Data Platforms

You'll be making high-level architectural decisions, evaluating new technologies, and ensuring our data infrastructure can handle exponential growth. The challenge of building robust, future-proof systems for a complex business truly excites you.

Successfully migrating a legacy data warehouse to a modern cloud-native platform (e.g., Snowflake), improving performance by 50% and reducing maintenance overhead.

What frustrates people
  • Navigating complex organisational politics to get buy-in for critical data initiatives.
  • Dealing with legacy systems and technical debt that slow down progress, despite your best efforts.
  • Recruiting and retaining top-tier talent in a highly competitive market.
  • Balancing the need for long-term strategic investments with urgent, short-term business demands.
  • The constant challenge of defining and enforcing data governance standards across a large, diverse organisation.
What this role does not give you
  • Extensive hands-on coding or individual contributor work.
  • A predictable, unchanging technical environment.
  • A role where you can avoid difficult conversations or strategic negotiations.
  • A 'set it and forget it' data platform – it's a living, evolving beast.

8Who you work with

This role directly impacts the strategic direction and operational efficiency of a major business unit. Your decisions will influence millions of pounds in revenue, drive cost efficiencies, and shape the data culture across hundreds of employees. You're essentially building the data nervous system for a significant part of our company.

Inside the business
  • C-Suite (CEO, COO, CFO, CMO)
  • Business Unit GMs and VPs
  • Product & Engineering Leadership
  • Finance & Legal Departments
  • Security & Compliance Teams
Outside the business
  • Key Technology Vendors (e.g., Snowflake, dbt Labs)
  • Industry Bodies & Standards Organisations
  • External Auditors & Regulators
  • Strategic Partners & Integrators

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • A proven track record of 5+ years leading and scaling large data engineering or analytics engineering organisations (25+ people, including managers).
  • Demonstrable experience owning and managing multi-million-pound budgets for data platforms and teams.
  • Extensive experience defining and executing enterprise-level data strategies that have delivered significant business impact.
  • Deep expertise in cloud data warehousing (e.g., Snowflake, Databricks) and modern ELT frameworks (e.g., dbt) at an architectural level.
  • Exceptional executive presence, communication, and influencing skills, with a track record of successfully engaging C-suite and Board-level stakeholders.

10What to practise next

Where the job is going, and what to do about it starting this week.

Real-time Analytics & Streaming Architectures

The demand for immediate insights is growing exponentially. Moving beyond batch processing to architecting and governing real-time data ingestion, processing, and serving layers will be crucial for competitive advantage.

Event-driven architectures · Stream processing frameworks (e.g., Kafka, Flink) · Low-latency data serving layers · Real-time data quality & observability

  • This quarter: Research leading real-time data platforms and their use cases in our industry.
  • Next quarter: Identify a business problem that would significantly benefit from real-time analytics and scope a pilot project.
  • Month 6: Engage with your technical leads to understand the practical challenges and opportunities of implementing streaming solutions.
  • Month 9: Develop a strategic roadmap for incorporating real-time capabilities into your business unit's data platform.

Quick win: Identify one existing batch report that causes significant delays. Explore how a simpler, near-real-time version could be built with existing tools or a small pilot.

11Staying current once you are in

What people here do to keep up
  • Active participation and speaking engagements at industry conferences (e.g., Data + AI Summit, Fivetran Modern Data Stack Conference, dbt Labs Coalesce).
  • Mentoring emerging leaders within the data community, both internally and externally.
  • Contributing to open-source data projects or publishing thought leadership articles on data strategy and architecture.
  • Regularly engaging with C-suite peers and industry analysts to stay abreast of market trends and challenges.
  • Enrolling in advanced executive education programmes focused on digital transformation, AI strategy, or organisational leadership.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Data Mesh / Data Fabric Strategic Implementation

Organisations are moving away from centralised data lakes to more distributed, domain-oriented data architectures. Understanding how to strategically implement and govern these complex paradigms is becoming critical for managing data at scale.

  • Domain-oriented data ownership
  • Data product thinking
  • Self-serve data infrastructure
  • Federated computational governance

Ethical AI & Data Bias Mitigation

As AI becomes more embedded in data products, the ethical implications and potential for bias are under increasing scrutiny. Leaders must understand how to build and govern data systems that are fair, transparent, and compliant, avoiding reputational and regulatory risks.

  • Algorithmic fairness metrics
  • Explainable AI (XAI)
  • Data provenance & auditability
  • Privacy-preserving AI techniques

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Enterprise Data Architecture & Strategy
  • Advanced Data Governance & Lineage
  • ELT (Extract, Load, Transform) at Scale
  • Data-as-Code & DevOps for Data
  • Cloud Data Platform Optimisation

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Analytics Engineering Manager / Senior Manager

    3-5 years in management roles leading multiple teams.

    Skills to master

    • Scaling teams and processes, managing managers, strategic planning for a large domain, cross-functional leadership, budget management for a significant department.

    You're ready to move on when

    • Successfully grew a team from 10 to 30+ people, including hiring and developing managers.
    • Owned a departmental budget of £500K+ and demonstrated clear ROI.
    • Led a major data platform initiative (e.g., a migration or significant architectural overhaul) from conception to delivery.
    • Consistently received strong feedback on strategic thinking and executive communication from senior leadership.
  2. 2

    Staff / Principal Analytics Engineer

    5-8 years as a top-tier individual contributor, leading technical strategy.

    Skills to master

    • Enterprise data architecture, technical strategy definition, influencing without direct authority, deep expertise in multiple data domains, mentoring and elevating other senior ICs.

    You're ready to move on when

    • Architected and delivered multiple foundational data systems used by hundreds of engineers/analysts.
    • Recognised as the go-to technical expert for complex, cross-cutting data challenges across the organisation.
    • Successfully influenced C-suite decisions on data technology investments through technical leadership.
    • Mentored and elevated multiple senior engineers, demonstrating leadership beyond direct management.
  3. 3

    Director of Data Science / Data Engineering

    Lateral move, 1-3 years in a similar Director role.

    Skills to master

    • Deepening understanding of Analytics Engineering domain, adapting leadership style to new team dynamics, building relationships with new executive stakeholders, understanding the specific business unit's challenges.

    You're ready to move on when

    • Proven track record leading a large data function in a similar industry or company size.
    • Demonstrated ability to quickly grasp new technical domains and translate them into strategic initiatives.
    • Strong references from executive peers on collaborative leadership and strategic impact.
    • A clear vision for how to integrate and elevate the Analytics Engineering function within our specific context.

15Where this role leads

The long view: This role can lead to positions such as Chief Data Officer or Chief Technology Officer, shaping data or technology strategy across a whole organisation.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Director, Analytics Engineering is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • VP of Data / Chief Data Officer (CDO)
  • Chief Technology Officer (CTO) / Chief Product Officer (CPO)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Analysis and Visualisation, worked against Director, Analytics Engineering.

Data Analysis and Visualisation Level 7

Applied to your work in Director, Analytics Engineering

1. To enable the learner to critically analyse the theoretical underpinnings of data analytics and their impact on decision-making in business management contexts. 2. To enable the learner to assess diverse data analysis activities, techniques, and tools applicable to business management scenarios. 3. To enable the learner to compare and contrast various predictive analytic techniques, evaluating their strengths and weaknesses in forecasting future business events. 4. To enable the learner to evaluate how predictive analytic techniques can be practically implemented for forecasting purposes within the business sector. 5. To enable the learner to evaluate prescriptive analytic techniques, illustrating their application with relevant examples from the business management domain. 6. To enable the learner to apply a suitable programming language or data analysis tool to conduct data analysis and visualisation tasks related to business management problems.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration
Director, Analytics Engineering
  • Enterprise Data Architecture & Strategy
  • Advanced Data Governance & Lineage
  • ELT (Extract, Load, Transform) at Scale
  • Data-as-Code & DevOps for Data
  • Cloud Data Platform Optimisation
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Director, Analytics Engineering is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. VP of Data / Chief Data Officer (CDO)

    3-5 years in the Director role.

    From leading a business unit's data strategy to owning the enterprise-wide data strategy, governance, and culture across all business units.

    • Global data architecture design (multi-region, multi-cloud)
    • Advanced data monetisation strategies
    • Ethical AI & responsible data usage at an enterprise scale
    • Building strategic partnerships with external data providers

  2. Chief Technology Officer (CTO) / Chief Product Officer (CPO)

    5-8 years in the Director role, often with a broader technology remit.

    Transitioning from a data-specific leadership role to owning the entire technology or product strategy for the company.

    • Large-scale software development lifecycle management
    • DevOps & SRE for all production systems
    • Global infrastructure & cloud operations
    • Intellectual property strategy & patent portfolio management
Working with AI on the job

Working with AI

Where AI is starting to help

As a Director, your time is incredibly valuable. Imagine if you could offload some of the heavy lifting—from strategic planning to executive communications—and focus even more on high-impact leadership. AI isn't just for junior roles; it's a game-changer for executive productivity.

We're not talking about replacing your strategic brain, but augmenting it. AI tools can help you synthesise vast amounts of information, draft complex documents, and even streamline how you manage your teams. This means less time on administrative overhead and more time shaping our data future.

Strategic Synthesis & Planning AI

Feed an AI tool market research, internal performance reports, and competitor analysis. It can then synthesise key trends, identify strategic opportunities, and even draft initial outlines for your multi-year data roadmap or business unit strategy. It's like having a highly efficient research assistant.

Executive Communication Drafting

Need to draft a board presentation, a strategic proposal for the C-suite, or a critical email to a major vendor? Use AI to generate initial drafts, refine your messaging, and ensure clarity and conciseness. This helps you communicate complex data strategies effectively and efficiently.

Team Performance & Bottleneck Analysis

Integrate AI with your team's project management and code repositories. It can analyse workflows, identify potential bottlenecks in your data pipelines, suggest process improvements, and even flag areas where team members might need additional support or training. This helps you optimise your organisation's output.

Stakeholder Alignment & Negotiation Prep

Use AI to help prepare for complex stakeholder meetings or negotiations. Input the context, the different parties' objectives, and potential points of contention. The AI can then suggest potential compromises, communication strategies, and even anticipate counter-arguments, giving you an edge in securing buy-in.

Common questions

Common questions

How do you become a Director, Analytics Engineering?

Common routes in include Analytics Engineering Manager / Senior Manager (3-5 years in management roles leading multiple teams.), Staff / Principal Analytics Engineer (5-8 years as a top-tier individual contributor, leading technical strategy.) and Director of Data Science / Data Engineering (Lateral move, 1-3 years in a similar Director role.). Times vary with prior experience.

Where can a Director, Analytics Engineering progress to?

This role can lead on to VP of Data / Chief Data Officer (CDO) (3-5 years in the Director role.) and Chief Technology Officer (CTO) / Chief Product Officer (CPO) (5-8 years in the Director role, often with a broader technology remit.), depending on the skills you build.

What new skills matter most for a Director, Analytics Engineering?

Increasingly, Data Mesh / Data Fabric Strategic Implementation and Ethical AI & Data Bias Mitigation. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

17Where to go from here

Other roles at Level 7

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built in this role transfer to Director or VP roles in other technology companies, to consultancies focused on data strategy, or to setting up an independent data venture.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free Data Fluency module What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.