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

Director, Data Platform 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.

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Experience bandDirector/VP (16-20 years)
Direct reports25-100+ reports
Reports toVP, Data / Chief Data Architect
UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as Head of Data Engineering · VP of Data Platforms · Senior Director, Big Data · Director, Enterprise Data Architecture

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 4 of 10 standards

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 3 of 10 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 3 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

NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science) NOCN · Level 5 · 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, Data Platform Engineering

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

Data Analysis and Visualisation OTHM Qualifications · covers 4 of 10 standards Level 7
Introduction to Data Science and Big Data NCC Education Limited · covers 3 of 10 standards Level 5
Data Analytics Pearson Education Ltd · covers 3 of 10 standards Level 5
Data-led Decision Making Institute of Sales Professionals · covers 1 of 10 standards Level 6
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, Data Platform 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

Ethical AI & Data Governance Leadership
Data Mesh / Data Product Leadership

Why each one is rising

The future skills this job leans on

AI Fluencyworking with AI as a thinking partner
Data Fluencyturning numbers into a decision
AI Governance Fluencyusing AI responsibly
Systems Fluencyseeing the whole picture

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, Data Platform 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

This role leads the technical vision for a data platform, setting direction, building and mentoring teams, and owning a budget rather than writing code day to day.

4What you'd actually use

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

Apache Spark (Enterprise Strategy & Governance) Expert

Setting enterprise standards for Spark usage (e.g., Databricks vs. self-managed EMR/Kubernetes), defining cost control policies, approving library management strategies, and making strategic decisions on cluster configurations for optimal performance and cost across the organisation.

Snowflake (Enterprise Governance & Cost Management) Expert

Governing enterprise Snowflake usage, making strategic decisions on multi-cluster architecture, implementing advanced cost management strategies (e.g., resource monitors), and overseeing integration with data governance tools like Collibra for metadata and access control.

Apache Airflow (Platform Management & CI/CD) Expert

Managing the Airflow platform itself, including multi-tenant deployments, executor strategies (Celery vs. Kubernetes), and setting enterprise standards for DAG development, testing, and CI/CD processes to ensure robust and reliable data orchestration.

Apache Kafka (Enterprise Event Streaming Backbone) Expert

Architecting the enterprise event-streaming backbone, making strategic decisions on Kafka deployment (Confluent Cloud vs. self-hosted), designing disaster recovery plans, and implementing multi-region replication strategies (e.g., MirrorMaker) for global data consistency and resilience.

AWS (Entire Data Ecosystem & Governance) Expert

Designing secure, scalable, and cost-effective multi-account data architectures on AWS. This involves governing network policies (VPCs, private links), making strategic choices between services (e.g., Kinesis vs. Kafka, Redshift vs. Snowflake), and ensuring compliance with security best practices across the entire data ecosystem.

Python & SQL (Language & Platform Strategy) Expert

Setting coding standards and library versions for the organisation, evaluating and championing new programming languages or data processing frameworks (e.g., Rust for performance-critical components, Polars as a Pandas alternative), and ensuring the technical excellence of our data engineering practices.

Terraform / CloudFormation (Infrastructure as Code Governance) Advanced

Defining and governing the Infrastructure as Code (IaC) strategy for all data platform components, ensuring consistency, auditability, and security across all cloud deployments. You'll be reviewing and approving major IaC changes and setting the standards for automation.

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 choice	Coming in	Where you are now	The step above
Data Platform Architecture	No independent decisions. Follows established patterns.	Chooses appropriate design patterns for specific components within existing architecture.	Designs and proposes new architectural patterns for complex workstreams, with peer review.
Budget Allocation & Spend	No budget authority. Reports on resource usage.	Manages personal cloud resource usage for projects.	Proposes resource allocation for specific projects up to £5K, with manager approval.
Team Organisation & Talent	No team decisions. Focuses on individual contribution.	Provides informal guidance to new joiners.	Mentors 0-2 junior engineers, contributes to hiring panels.

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.

Platform Stability & Uptime

Reduction in critical (P1/P2) data incidents across the entire enterprise data platform.

Target · Reduce P1/P2 incidents by >30% year-over-year.

After implementing new monitoring and architectural changes, the number of P1/P2 incidents related to data pipelines dropped from 10 in Q1 to 6 in Q1 the following year, exceeding the 30% target.

Cloud Cost Optimisation

Optimisation of compute and storage costs for the data platform, while maintaining or improving performance.

Target · Achieve a >15% reduction in data platform cloud spend (e.g., AWS, Databricks, Snowflake) year-over-year, excluding growth-related increases.

Through strategic re-platforming and resource management, you cut the annual Snowflake spend by £2M, representing a 20% saving against the previous year's baseline, even with increased data volume.

Business Enablement & New Capabilities

Successful launch and adoption of new data platform capabilities that enable measurable business outcomes.

Target · Launch at least two major new platform capabilities annually that directly contribute to specific business unit goals (e.g., £10M in new revenue, 20% reduction in customer churn, 5% increase in operational efficiency).

You launched a new real-time analytics capability that allowed the Marketing team to optimise campaign spend, leading to an additional £12M in attributable revenue within six months.

Team Health & Retention

Maintaining a healthy, engaged, and high-performing engineering organisation.

Target · Achieve team attrition rate below the industry average (typically <10%) and maintain >85% team engagement scores in annual surveys.

Your data platform engineering teams consistently report high satisfaction, with voluntary attrition at 8% (below the 12% industry average) and an engagement score of 88%, reflecting strong leadership and a positive work environment.

Strategic Influence & Thought Leadership

Being seen as a trusted advisor and thought leader on data strategy by C-Suite and business unit leaders.

Regularly invited to C-Suite strategic planning sessions
opinions sought on major technology investments
presenting at industry conferences or internal leadership summits
proactive identification of future data trends and risks.

Organisational Design & Talent Development

Building and evolving a highly effective data engineering organisation, including succession planning and career development.

Successful hiring and retention of senior talent
clear career pathways established for engineers
internal promotions from within your teams
positive feedback from direct reports and skip-levels on mentorship and growth opportunities
effective reorganisation to meet evolving business needs.

Cross-Functional Collaboration & Alignment

Driving alignment and effective collaboration between data engineering and other critical functions (Product, Security, Business Units).

Data platform roadmap is clearly aligned with business priorities
joint planning sessions with Product and Security
positive feedback from peer leaders on collaborative efforts
successful resolution of cross-functional conflicts without escalation.

Risk Management & Governance

Proactive identification, mitigation, and communication of risks related to data security, privacy, and compliance.

No major data breaches or compliance fines under your remit
comprehensive data governance frameworks implemented and adopted
regular reporting on data risk posture to executive leadership
successful navigation of complex regulatory changes (e.g., GDPR updates).

7Would you like it

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

What people enjoy

Building for Scale and Impact

You're energised by the idea of designing systems that handle petabytes of data and support thousands of users globally. You love seeing your architectural decisions enable new business capabilities or solve critical organisational challenges. The thought of a robust, high-performing data platform that underpins the entire business truly excites you.

Leading the re-architecture of our core data lake to support real-time analytics for a new product line, knowing it will unlock millions in potential revenue.

Developing and Empowering Teams

You get a real kick out of seeing your direct reports grow into stronger leaders and engineers. You enjoy mentoring, coaching, and creating an environment where people feel empowered to take ownership and innovate. You're motivated by building a high-performing, cohesive team that can tackle any challenge.

Successfully developing a senior staff engineer into a team lead, or seeing one of your managers launch a major initiative with minimal oversight.

Strategic Problem Solving

You thrive on tackling complex, ambiguous, and often political problems that have no easy answers. You enjoy stepping back from the day-to-day to think about the long-term implications of technical choices, organisational structures, and market trends. You're motivated by the intellectual challenge of shaping a large-scale technical strategy.

Designing a global data residency strategy that balances compliance, performance, and cost across multiple cloud regions, involving legal, security, and engineering teams.

What frustrates people

Navigating organisational politics to get buy-in for critical platform investments.
Balancing the need for innovation with the reality of technical debt and legacy systems.
Recruiting and retaining top-tier talent in a highly competitive market.
Explaining the true cost and complexity of data engineering to non-technical executives.
Dealing with the 'Upstream Surprise' (an API change breaking your pipelines) at a global scale, leading to widespread impact.
The 'Hype Cycle Treadmill' – constantly evaluating and fending off requests for the 'next big thing' in data technology without a clear business case.
Managing large budgets and justifying every pound spent to Finance, especially during economic downturns.

What this role does not give you

Daily hands-on coding or deep technical implementation (you'll be overseeing, not doing).
A predictable, routine work schedule (expect strategic shifts and occasional urgent escalations).
Isolation from organisational politics (you'll be at the heart of it).
The luxury of avoiding difficult conversations or making unpopular decisions.
A role where you can avoid budget discussions or financial accountability.

8Who you work with

This role directly impacts the organisation's ability to make data-driven decisions, innovate new products, and operate efficiently. You'll be accountable for the performance, cost, security, and scalability of all critical data infrastructure, directly influencing multi-million-pound budgets and the strategic direction of entire business units. A strong leader here enables significant revenue growth and cost savings, while a weak one can lead to critical business failures and reputational damage.

Inside the business

C-Suite (CEO, CTO, COO, CFO)
Business Unit VPs and Directors (e.g., Marketing, Sales, Product)
Heads of Engineering and Architecture
Cyber Security and Legal Teams
Finance Leadership

Outside the business

Strategic Technology Vendors (e.g., Databricks, Snowflake, Confluent)
Industry Peers and Standards Bodies
Consulting Partners
Potential M&A targets (for technical due diligence)

9What you need before you start

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

Extensive experience (12-16 years minimum) in building and managing large-scale data platforms and distributed systems, ideally within a global enterprise.
Proven track record of leading and mentoring multiple engineering teams, including managers and senior individual contributors.
Demonstrable experience in defining and executing technical strategy for a significant business unit or department.
Deep expertise in at least one major cloud provider's data ecosystem (AWS, Azure, or GCP) at an architectural level.
Strong financial acumen, including experience managing multi-million-pound budgets and optimising cloud spend.
Excellent executive communication and stakeholder management skills, with a history of influencing C-Suite and Board-level decisions.
A Master's degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience that demonstrates expert-level technical and leadership capabilities.

10What to practise next

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

Advanced Data Observability & AIOps for Data

As data platforms grow in complexity and scale, manual monitoring and incident response become unsustainable. AIOps for data uses machine learning to automatically detect anomalies, predict failures, and even suggest resolutions, moving us from reactive to proactive operations. This is critical for maintaining high platform stability and reducing operational costs.

Anomaly Detection (Data & System) · Root Cause Analysis (Automated) · Predictive Maintenance (Data Platforms) · Automated Remediation · Data Quality Firewalls

This quarter: Research leading AIOps platforms for data (e.g., Datafold, Monte Carlo) and assess their fit for our environment.
This month: Work with your SRE/Ops teams to identify the top 3 recurring data platform incidents and explore how AI could have prevented or resolved them faster.
Next quarter: Sponsor a proof-of-concept for an AIOps solution on a critical data pipeline.
Month 4-6: Develop a strategy for integrating AIOps into our broader data platform operational model.
Ongoing: Stay updated on advancements in ML for operations and data quality.

Quick win: Implement automated anomaly detection on a key data quality metric for a critical dashboard. It's a small step that can yield immediate benefits in trust and reliability.

Quantum Computing Impact on Data Processing

While still nascent, quantum computing has the potential to revolutionise data processing for certain complex problems (e.g., optimisation, cryptography, large-scale simulations). As a Director, you need to be aware of its long-term implications, even if it's not a day-to-day concern yet, to inform future R&D and strategic investments.

Quantum Algorithms (e.g., Grover's, Shor's) · Quantum Machine Learning · Quantum Cryptography · Quantum Hardware Limitations · Hybrid Quantum-Classical Computing

This quarter: Read introductory materials on quantum computing for data science and engineering.
This month: Attend a webinar or virtual conference on the future of quantum computing in enterprise.
Next quarter: Identify one specific, currently intractable data problem within our organisation that might eventually be solvable by quantum computing.
Month 4-6: Engage with academic researchers or industry experts in quantum computing to understand the realistic timeline for enterprise adoption.
Ongoing: Monitor key breakthroughs and investments in the quantum computing space.

Quick win: Allocate a small 'innovation budget' for your teams to explore quantum computing concepts, perhaps through online courses or hackathons, to build early awareness and expertise.

11Staying current once you are in

What people here do to keep up

Regularly attend and present at industry conferences (e.g., Strata Data, Data + AI Summit, AWS Re:Invent) to stay abreast of emerging trends and network with peers.
Actively participate in leadership development programmes, focusing on executive presence, strategic influence, and organisational change management.
Mentor junior and mid-level engineers and managers, fostering a culture of continuous learning and knowledge sharing within your teams.
Contribute to open-source projects or publish technical articles/whitepapers, establishing yourself as a thought leader in the data community.
Engage with academic institutions or research labs on cutting-edge data technologies and their potential applications.

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.

Ethical AI & Data Governance Leadership

As AI becomes more pervasive, the ethical implications of data usage, model bias, and algorithmic transparency are becoming paramount. Regulators, customers, and employees are demanding greater accountability. Leaders who can navigate this complex space will build trust and avoid significant reputational and legal risks.

AI Ethics Frameworks
Explainable AI (XAI)
Data Bias Detection & Mitigation
Algorithmic Auditing
Privacy-Enhancing Technologies (PETs)

Data Mesh / Data Product Leadership

Traditional centralised data platforms often become bottlenecks in large, distributed organisations. The data mesh paradigm, with its focus on decentralised data ownership and 'data as a product,' is gaining traction as a way to scale data capabilities. Leading this shift requires a complete rethink of organisational structure, technical architecture, and cultural norms.

Domain-Oriented Data Ownership
Data as a Product
Self-Serve Data Platform
Federated Computational Governance
Data Product Lifecycle Management

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Distributed Computing Principles (Strategic Application)
Advanced Data Modelling (Enterprise Governance)
ETL/ELT Design Patterns (Platform Strategy)
Data Governance & Lineage (Organisational Implementation)
System Performance Tuning (Strategic Optimisation)
Cloud Data Architecture (Enterprise Design & Governance)

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
Principal Big Data Engineer
3-5 years as a Principal
Skills to master
- Deep expertise in enterprise-scale data architecture, ability to influence technical direction across multiple teams, strong mentorship skills for senior engineers, and a proven track record of solving the most complex technical challenges.
You're ready to move on when
- Successfully architected and delivered at least two major, cross-functional data initiatives from conception to production.
- Consistently sought out by other teams for technical guidance and problem-solving.
- Has informally led technical strategy for a significant domain or platform component.
- Demonstrates strong communication skills with both technical and non-technical audiences, including senior leadership.
2
Senior Manager, Data Engineering
3-5 years as a Senior Manager
Skills to master
- Proven ability to manage multiple data engineering teams, strong people leadership and development skills, experience owning a significant team budget, and a track record of delivering complex projects through others.
You're ready to move on when
- Successfully managed and grown a team of 10+ data engineers (including other managers).
- Consistently met or exceeded project delivery targets for their teams.
- Demonstrates strong financial acumen and experience with budget planning and optimisation.
- Has effectively navigated organisational challenges and influenced peer leaders to achieve common goals.
3
Head of Data Architecture
4-6 years as Head of Architecture
Skills to master
- Enterprise-wide architectural design and governance, strategic technology evaluation and selection, ability to define and communicate a coherent long-term architectural vision, and strong influence across all engineering functions.
You're ready to move on when
- Has designed and implemented a significant portion of the current enterprise data architecture.
- Is the recognised authority on architectural standards and best practices.
- Consistently provides strategic architectural guidance to senior leadership and other engineering teams.
- Has successfully driven the adoption of new architectural patterns or technologies across the organisation.

15Where this role leads

The long view: This work can lead to roles such as Chief Data Officer or Chief Information Officer, overseeing data strategy or IT operations across a business.

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, Data Platform Engineering is actually changing.

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, Data Platform Engineering.

Data Analysis and Visualisation Level 7

Applied to your work in Director, Data Platform 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 Remember
2 Understand
3 Apply
4 Analyse
5 Evaluate
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, Data Platform Engineering

Distributed Computing Principles (Strategic Application)
Advanced Data Modelling (Enterprise Governance)
ETL/ELT Design Patterns (Platform Strategy)
Data Governance & Lineage (Organisational Implementation)
System Performance Tuning (Strategic Optimisation)
Cloud Data Architecture (Enterprise Design & Governance)

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

VP, Data / Chief Data Architect
3-5 years in Director role

This is the next logical step, moving from leading a business unit's data platform to defining the company-wide data strategy and vision.
- Enterprise data governance and regulatory compliance strategy.
- Global data ecosystem architecture (spanning multiple business units and regions).
- Advanced cloud economics and vendor partnership strategy at a corporate level.
- Innovation portfolio management for data and AI technologies.
Chief Technology Officer (CTO)
5-8 years in Director/VP role

A significant leap to overseeing all technology functions across the entire company, not just data.
- Enterprise software development lifecycle and quality assurance.
- Global IT operations and infrastructure management.
- Comprehensive cyber security strategy and risk management.
- Digital transformation leadership across all business functions.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, as a Director, your time is precious. You're juggling strategic planning, team leadership, budget management, and executive communication. Imagine if you could offload some of the heavy lifting, giving you more headspace for the truly critical decisions. That's where AI comes in.

AI isn't going to replace your strategic mind, but it can be an incredibly powerful co-pilot. For a Director of Data Platform Engineering, AI tools can help you synthesise complex information, accelerate decision-making, and even streamline your communication, freeing you up to focus on the big picture and lead your teams more effectively. It's about augmenting your leadership, not automating it.

Strategic Data Analysis & Synthesis

Feed AI models complex reports, market analyses, and internal performance data. Ask it to identify key trends, summarise risks, or propose strategic options for platform evolution. It won't make the decision for you, but it'll give you a highly condensed, insightful starting point for your own analysis. Think of it as having an army of junior analysts doing the grunt work in seconds.

Executive Communication Drafts

Need to draft a board presentation on data platform strategy? Or an email to the C-Suite explaining a major incident? Use AI to generate initial drafts, summarise key points, or refine your language for clarity and impact. This isn't about letting AI write for you, but about getting a strong first pass that you can then tailor with your unique insights and tone. It's a huge time-saver for high-stakes communications.

Cloud Cost Optimisation Insights

Integrate AI with your cloud billing data. Ask it to identify anomalies in spend, pinpoint underutilised resources, or suggest architectural changes that could lead to significant cost savings. It can highlight areas where your teams might be over-provisioning or where specific workloads are becoming unexpectedly expensive, helping you make data-driven budget decisions faster.

Roadmap & Strategy Validation

Upload your proposed data platform roadmap and ask an AI to identify potential risks, dependencies, or conflicts based on industry best practices and known technical challenges. You can even simulate different scenarios to understand the potential impact of various strategic choices, giving you more confidence in your decisions before committing significant resources.

Common questions

How do you become a Director, Data Platform Engineering?

Common routes in include Principal Big Data Engineer (3-5 years as a Principal), Senior Manager, Data Engineering (3-5 years as a Senior Manager) and Head of Data Architecture (4-6 years as Head of Architecture). Times vary with prior experience.

Where can a Director, Data Platform Engineering progress to?

This role can lead on to VP, Data / Chief Data Architect (3-5 years in Director role) and Chief Technology Officer (CTO) (5-8 years in Director/VP role), depending on the skills you build.

What new skills matter most for a Director, Data Platform Engineering?

Increasingly, Ethical AI & Data Governance Leadership and Data Mesh / Data Product Leadership. These are the skills our record of this role lists as growing.

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.

Director, Data Platform Engineering

Where should we send it?

Two short quizzes about you

Will AI change your work as a Director, Data Platform Engineering?

What's rising in its place

Ethical AI & Data Governance Leadership

Data Mesh / Data Product Leadership

Skills this role draws on

Technical

How you actually get there, here

Principal Big Data Engineer

Senior Manager, Data Engineering

Head of Data Architecture

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

Roles this leads to

One to one, on the work you already do

Everything else the record holds

The career path, and where it branches

VP, Data / Chief Data Architect

Chief Technology Officer (CTO)

Where AI is starting to help

Strategic Data Analysis & Synthesis

Executive Communication Drafts

Cloud Cost Optimisation Insights

Roadmap & Strategy Validation

Common questions

Want a read on where you stand?