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

Director of Data 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 reports3-5 reports
Reports toVP of Engineering or Chief Technology Officer (CTO)
UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as VP of Data Platform · Head of Data Engineering · Data Platform Director

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

Automation Fluency building AI into how work runs

Your Plan Illustration

Built for Director of Data 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 of Data 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 Architecture
AI/ML Governance & MLOps Strategy

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
Automation Fluencybuilding AI into how work runs

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 of Data 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.

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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 Data Engineering oversees the strategy, health and future direction of how an organisation collects, processes and delivers data. This role leads multiple teams, shapes a multi-year technical roadmap and manages significant budgets, while making sure the data platform is scalable and useful. It is not a hands-on coding role, though fluency in the technical detail matters. The focus is on vision, people, and keeping the data engine running.

4What you'd actually use

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

Snowflake / Databricks Strategic Command

Making platform selection decisions, negotiating enterprise contracts, setting cross-functional data access and security policies, and overseeing credit consumption at an organisational level.

dbt / Airflow / Fivetran Architectural Oversight

Setting standards for dbt project structure, defining the enterprise orchestration strategy, and evaluating new tools for the ingestion and transformation stack to ensure scalability and efficiency.

AWS (S3, Glue, Redshift, EMR, Lambda, IAM) Strategic Financial & Architectural Management

Managing multi-million-pound cloud budgets, setting enterprise security posture, and deciding on architectural patterns (e.g., Lakehouse vs. Warehouse) for the entire data platform.

Terraform Strategic Implementation

Mandating Infrastructure as Code (IaC) for all data infrastructure, setting policy-as-code using Sentinel, and governing the overall cloud environment structure to ensure consistency and compliance.

Python (PySpark, Pandas) & SQL Sets Coding Standards & Best Practices

Championing code quality, defining testing frameworks, and ensuring the team's technical skills align with strategic goals. You'll review high-level designs and ensure robust engineering principles are applied.

Data Catalogues (e.g., Collibra, Alation) Expert

Defining the strategy for data discoverability, lineage, and metadata management across the organisation, ensuring data assets are well-understood and governed.

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 & Technology Selection	Follows established patterns, escalates any deviation.	Proposes solutions for specific problems within existing architecture, seeks approval.	Designs and recommends new architectural components, makes technical decisions within workstream scope, consults Lead/Director on major changes.
Organisational Design & Talent Management	No authority.	Provides informal feedback, participates in interview panels.	Mentors junior engineers, leads interview loops, provides input on team structure.
Budget Allocation & Cloud Spend	No authority.	Aware of resource consumption, flags potential cost overruns.	Optimises specific pipeline costs, recommends cost-saving measures.

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.

Cloud Cost Optimisation (Data Platform)

Reducing the total expenditure on cloud resources specifically for the data platform (e.g., Snowflake credits, AWS S3/EC2/EMR costs).

Target · Achieve a 15% year-on-year reduction in data platform cloud spend while maintaining or improving performance and data freshness SLAs.

In Q2, we reduced Snowflake credit consumption by 18% compared to the previous year, primarily through optimising warehouse sizes and implementing better query governance. That's a saving of roughly £500K annually.

Data Platform Uptime & Reliability

Ensuring the core data platform and critical data pipelines are consistently available and operational.

Target · Maintain 99.9% uptime for the core data platform and 99.5% success rate for all critical production data pipelines.

Last month, we had one P1 incident on our core ingestion service, which was resolved within 30 minutes, keeping our monthly uptime at 99.95%.

Business Enablement & Value Creation

Directly contributing to new revenue opportunities or significant cost savings through data platform capabilities.

Target · Deliver data platform capabilities that enable at least £2M in new revenue or cost savings annually.

Our new real-time customer segmentation data feed, built by your teams, directly enabled the Marketing team to launch a targeted campaign that generated an additional £2.5M in Q3 revenue.

Team Health & Retention

Measuring the overall well-being, engagement, and stability of the data engineering organisation.

Target · Maintain an annual voluntary attrition rate below 10% for the data engineering department and achieve an average employee engagement score of 80% or higher.

Our Q3 engagement survey showed an 82% satisfaction rate, and we've only had one voluntary departure in the last 12 months across a team of 30 engineers.

Strategic Influence & Thought Leadership

Being recognised as a trusted advisor and visionary for data strategy across the organisation and, ideally, within the broader industry.

You're regularly invited to C-suite strategy sessions
your opinions are sought on major business initiatives
you present at industry conferences or publish thought leadership pieces
your team's work is cited as a benchmark internally.

Data Governance & Quality Maturity

Driving the organisation's maturity in managing data as an asset, ensuring its quality, security, and compliance.

We have clear, adopted data ownership models
data quality issues are proactively identified and resolved
audit findings related to data are minimal
the business trusts the data for critical decisions
the data catalogue is comprehensive and well-used.

Talent Development & Organisational Growth

Building a strong, resilient data engineering organisation by fostering growth, promoting from within, and attracting top talent.

Managers under your leadership are effectively developing their teams
we're seeing internal promotions into more senior roles
our hiring pipeline is strong
engineers feel challenged and supported
the team is seen as a desirable place to work.

Cross-Functional Collaboration & Partnership

Establishing strong, productive relationships with other departments, ensuring data engineering is seen as an enabler, not just a service provider.

Product, Analytics, and Operations teams proactively involve data engineering in their early planning stages
there's a clear understanding of data engineering's capabilities and limitations
joint initiatives consistently deliver on time and budget
feedback from other departments is overwhelmingly positive about collaboration.

7Would you like it

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

What people enjoy

Building a Lasting Legacy

You're driven by the idea of creating a data platform that will serve the company for years to come, not just delivering quick wins. You'll spend time on foundational architectural decisions, even if they're not immediately visible, because you know they're critical for long-term stability and scalability.

You'll champion the adoption of a robust data contract framework, knowing it will prevent countless pipeline breaks in the future, even if it means slowing down initial development slightly.

Empowering Others to Succeed

Your greatest satisfaction comes from seeing your managers and their teams grow, take on bigger challenges, and deliver impactful work. You'll invest heavily in mentorship, coaching, and creating opportunities for your people.

You'll delegate a major platform upgrade to one of your managers, providing strategic guidance but letting them own the execution, knowing it's a huge growth opportunity for them.

Driving Strategic Business Impact

You're motivated by how your data platform directly contributes to the company's bottom line, whether it's enabling new product features, optimising operations, or informing executive decisions. You want to see your work move the business forward, not just tick technical boxes.

You'll actively engage with the CPO to understand their 3-year product roadmap, ensuring your data strategy is perfectly aligned to support future innovations and market opportunities.

What frustrates people

Endless Political Battles: Getting different departments (e.g., Marketing, Finance, Product) to agree on a single source of truth or a common data definition can feel like herding cats. Everyone wants 'their' version of the numbers.
Legacy Systems & Technical Debt: Inheriting a complex, often brittle data architecture with years of accumulated technical debt, and constantly fighting for resources to modernise it while business demands keep piling up.
The 'Magic Button' Expectation: Other executives often see data engineering as a black box that should magically produce perfect, real-time data for any request, without understanding the underlying complexity or cost.
Talent Wars: The constant challenge of attracting, retaining, and developing top-tier data engineering talent in a highly competitive market.
Budget Scrutiny: Justifying multi-million-pound cloud spend to the CFO, especially when the direct ROI isn't always immediately obvious or quantifiable.
The 'Thankless' Nature: Your team works tirelessly to achieve 99.9% uptime and data freshness, but the only time the C-suite knows your name is during the 0.1% of the time when things are broken. It can feel like you're only noticed when there's a problem.

What this role does not give you

Daily hands-on coding or deep technical implementation (this is a leadership role, not an individual contributor one).
A perfectly clean, well-documented data landscape from day one (you'll be building and fixing simultaneously).
A quiet, uninterrupted environment for deep work (expect many meetings, strategic discussions, and urgent escalations).
The ability to please everyone (you'll often need to say 'no' or 'not yet' to requests).

8Who you work with

This role shapes the entire data strategy for a business unit or the entire company, directly influencing multi-year financial performance (P&L £2M-£10M+), market position, and the organisation's ability to innovate. You'll drive significant transformation, impacting everything from customer experience to internal operational efficiency. Your decisions here can literally make or break our ability to scale.

Inside the business

C-Suite (CEO, CFO, CPO)
VP of Product and Product Directors
VP of Operations and Supply Chain
Head of Analytics and Data Science
Information Security and Compliance Leadership
Legal Counsel

Outside the business

Key Technology Vendors (e.g., Snowflake, Databricks, AWS)
Industry Bodies and Standards Organisations
Strategic Integration Partners
Potential M&A Targets (for due diligence and integration planning)
Investors (occasionally for technical strategy briefings)

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 data engineering, with at least 5 years in a leadership role managing other managers or large teams.
Proven track record of defining and delivering multi-year data platform strategies that have driven significant business impact.
Deep architectural expertise in cloud-native data platforms (AWS, Azure, or GCP) and associated technologies (Snowflake, Databricks, Spark, Airflow, dbt).
Demonstrable experience managing multi-million-pound budgets and driving cloud cost optimisation initiatives.
Strong understanding of data governance, data quality, and data security principles at an enterprise level.
Exceptional communication, influencing, and stakeholder management skills, with experience presenting to C-suite and board members.
A history of building, mentoring, and scaling high-performing data engineering organisations.

10What to practise next

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

Advanced Cloud Platform Optimisation (Beyond FinOps)

Simply managing cloud costs isn't enough; you'll need to understand how to strategically architect for maximum performance, resilience, and cost-efficiency across multiple cloud services. This includes deep dives into specific service capabilities and their interplay.

Serverless data processing patterns (Lambda, Azure · Containerisation and orchestration (Kubernetes, EC · Data streaming architectures (Kafka, Kinesis): Des · Advanced networking for data platforms: Optimising · Cloud security best practices: Implementing least

This week: Review your current cloud spend reports and identify the top 5 cost drivers. Challenge your teams on optimisation opportunities.
This month: Attend a strategic workshop or webinar on advanced cloud architecture patterns for data.
Month 2: Engage directly with cloud vendor solution architects to understand their latest offerings and roadmap.
Month 3: Mandate a 'cloud efficiency review' for every major data project, ensuring cost and performance are baked into the design.

Quick win: Ensure all new data projects have clear cost monitoring and alerting set up from day one. Challenge your teams to justify every 'always-on' resource.

Data Product Management & API Design

As data becomes a product, you'll need to think like a product manager for your data assets. This means understanding user needs, defining clear APIs for data access, and ensuring your data products are discoverable, reliable, and well-supported.

User stories for data consumers: Understanding how · API-first approach for data access: Designing clea · Data contract definition: Formal agreements betwee · Data product lifecycle: From ideation to deprecati · Data catalogue integration: Ensuring data products

This week: Have a coffee chat with a Product Manager to understand their approach to product strategy and user needs.
This month: Select one key dataset and define it as a 'data product' with clear ownership, documentation, and an API specification.
Month 2: Work with your teams to implement a data contract for a critical upstream data source.
Month 3: Present the concept of 'data as a product' to your leadership team, outlining the benefits and next steps.

Quick win: Start by encouraging your teams to write clear, business-friendly descriptions for their most important tables and views, treating them as internal products.

11Staying current once you are in

What people here do to keep up

Active participation in industry conferences (e.g., Data + AI Summit, AWS re:Invent, Snowflake Summit) as an attendee or, ideally, a speaker.
Regular engagement with industry peer groups and executive forums to share best practices and stay abreast of emerging trends.
Mentoring aspiring data leaders, either formally or informally, to hone your leadership and coaching skills.
Contributing to open-source data projects or publishing technical articles/blog posts to establish thought leadership.
Undertaking executive leadership training or an MBA programme to further develop business acumen and strategic thinking.

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 Architecture

Organisations are struggling with centralised data platforms becoming bottlenecks. Data Mesh and Fabric offer decentralised, domain-oriented approaches to data ownership and delivery, promising greater agility and scalability. This is a strategic shift, not just a technical one.

Domain-oriented data ownership: Data as a product,
Self-serve data platform: Empowering domain teams
Federated computational governance: Decentralised
Data product thinking: Treating datasets as produc
Semantic layer integration: Unifying data definiti

AI/ML Governance & MLOps Strategy

As AI and Machine Learning models become embedded in critical business processes, the need for robust governance, explainability, and operationalisation (MLOps) becomes paramount. You'll need to ensure models are fair, compliant, and performant in production.

Model lifecycle management: From experimentation t
Explainable AI (XAI): Understanding why models mak
Fairness and bias detection: Ensuring models don't
Model drift detection: Monitoring model performanc
Feature stores: Centralised repositories for manag

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Advanced Data Modeling & Architecture
ETL/ELT Design Patterns & Orchestration Strategy
DataOps & CI/CD for Data Platforms
Enterprise Data Governance & Security
Cloud FinOps for Data Platforms
Distributed Systems Architecture & Resilience

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
Senior Data Engineering Manager
3-5 years in previous role
Skills to master
- Mastering people management, project delivery for multiple teams, advanced stakeholder management, and budget oversight for a significant function (£500K-£2M P&L).
You're ready to move on when
- Successfully led 2-3 major data platform initiatives from conception to delivery.
- Consistently met or exceeded team performance metrics and retention targets.
- Demonstrated strong ability to influence product and business strategy through data insights.
- Mentored and developed at least two direct reports into more senior roles.
2
Lead / Staff Data Engineer (Architectural Track)
4-6 years in previous role
Skills to master
- Deepening architectural expertise across the entire data ecosystem, solving the most complex technical challenges, influencing cross-team technical direction, and providing informal leadership/mentorship to a large group of engineers.
You're ready to move on when
- Architected and delivered multiple critical, large-scale data platform components.
- Recognised as the go-to technical expert for complex data challenges across the organisation.
- Successfully driven adoption of new technologies or architectural patterns.
- Consistently provided high-quality technical guidance and mentorship to senior engineers.
3
Head of Data / Data Platform (Smaller Organisation)
2-4 years in previous role
Skills to master
- Gaining holistic ownership of a data function in a smaller company, encompassing strategy, operations, and team building. This provides broad experience that can scale to a Director role in a larger organisation.
You're ready to move on when
- Built and scaled a data team from scratch or significantly grew an existing one.
- Owned the end-to-end data strategy and execution for a company.
- Successfully managed a full data budget and demonstrated clear ROI.
- Presented data strategy and performance directly to the CEO/Board.

15Where this role leads

The long view: This role can lead towards a Chief Data Officer or Chief Technology Officer position, or a board level role such as Non-Executive Director, drawing on experience built here.

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 of Data 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 of Data Engineering.

Data Analysis and Visualisation Level 7

Applied to your work in Director of Data 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 of Data Engineering

Advanced Data Modeling & Architecture
ETL/ELT Design Patterns & Orchestration Strategy
DataOps & CI/CD for Data Platforms
Enterprise Data Governance & Security
Cloud FinOps for Data Platforms
Distributed Systems Architecture & Resilience

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

VP of Data Platform / Chief Data Officer (CDO)
3-5 years in Director role

Executive Leadership (OFQUAL Level 8)
- Global data strategy and compliance (e.g., navigating international data residency laws).
- Advanced data monetisation strategies and business model innovation through data.
- Building and managing a data-centric P&L for an entire business unit.
- Industry thought leadership and external representation of the company's data vision.
VP of Engineering / CTO
4-6 years in Director role

Executive Leadership (OFQUAL Level 8)
- Software engineering best practices across the entire SDLC.
- Cybersecurity strategy for the entire technology estate.
- Vendor management and technology partnerships at an enterprise level.
- Driving innovation and R&D across all technical domains.

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 strategy, people, and a never-ending stream of urgent requests. What if you could reclaim a significant chunk of your week, not just for yourself, but for your entire organisation? AI isn't just for junior engineers anymore; it's a strategic enabler for leadership.

We're not talking about replacing your teams; we're talking about augmenting their capabilities and freeing up your strategic bandwidth. Imagine your teams spending less time on repetitive tasks and more time on high-impact, innovative projects. Here's how AI can transform how your data engineering organisation operates, giving you back valuable time to focus on vision and growth.

Automated Pipeline Scaffolding

Imagine your managers or lead engineers simply describing a new data source or integration requirement in natural language. AI can then analyse source schemas and generate the boilerplate Airflow DAGs, dbt models, and initial data quality tests. This means your teams spend less time on repetitive setup and more on complex logic and optimisation, accelerating delivery of new data products.

Intelligent Query Optimisation & Cost Management

AI can continuously scan your Snowflake or Databricks query history, automatically identifying inefficient queries that are burning through credits. It won't just flag them; it'll suggest specific rewrites, changes to clustering keys, or even recommend warehouse resizing. This translates directly into significant cloud cost savings, which, frankly, will make your CFO very happy.

Proactive Incident Root Cause Analysis (RCA)

When a critical pipeline fails, time is money. AI can ingest logs from Airflow, cloud providers, and monitoring tools, correlating events across systems to present a summary of the most likely root cause (e.g., 'Upstream API latency spike at 2:15 AM caused timeout in ingestion task'). This drastically reduces the time your on-call teams spend on frantic log-diving, getting services back online faster.

Self-Documenting Data Ecosystems

Let's be honest, documentation is often an afterthought. AI can scan all your dbt models, SQL queries, and BI tool reports to automatically generate and update data lineage graphs. Even better, LLMs can write business-friendly descriptions for tables and columns, keeping your enterprise data catalogue current with minimal manual effort. This improves data discoverability and trust across the entire business.

Common questions

How do you become a Director of Data Engineering?

Common routes in include Senior Data Engineering Manager (3-5 years in previous role), Lead / Staff Data Engineer (Architectural Track) (4-6 years in previous role) and Head of Data / Data Platform (Smaller Organisation) (2-4 years in previous role). Times vary with prior experience.

Where can a Director of Data Engineering progress to?

This role can lead on to VP of Data Platform / Chief Data Officer (CDO) (3-5 years in Director role) and VP of Engineering / CTO (4-6 years in Director role), depending on the skills you build.

What new skills matter most for a Director of Data Engineering?

Increasingly, Data Mesh / Data Fabric Architecture and AI/ML Governance & MLOps Strategy. 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 of Data Engineering

Where should we send it?

Two short quizzes about you

Will AI change your work as a Director of Data Engineering?

What's rising in its place

Data Mesh / Data Fabric Architecture

AI/ML Governance & MLOps Strategy

Skills this role draws on

Technical

How you actually get there, here

Senior Data Engineering Manager

Lead / Staff Data Engineer (Architectural Track)

Head of Data / Data Platform (Smaller Organisation)

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 of Data Platform / Chief Data Officer (CDO)

VP of Engineering / CTO

Where AI is starting to help

Automated Pipeline Scaffolding

Intelligent Query Optimisation & Cost Management

Proactive Incident Root Cause Analysis (RCA)

Self-Documenting Data Ecosystems

Common questions

Want a read on where you stand?