UK

United Kingdom · Technology and data · Mid-Level (2-5 years)

Data Engineer

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

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  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior Data Engineer or Data Engineering Manager
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Data Platform Engineer · ETL Developer · Analytics Engineer

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

NOCN Level 4 certificate for Data Analysts NOCN · Level 4 · covers 8 of 10 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards
Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 2 of 10 standards
Pearson BTEC Level 3 Diploma in Engineering and Manufacturing Support Technologies Pearson Education Ltd · Level 3 · covers 2 of 10 standards
NOCN Level 3 VRQ in Data Science NOCN · Level 3 · 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 Architecture NOCN Level 4 certificate for Data Analysts
Data Analytics Pearson BTEC Level 4 Higher National Certificate in Computing for England
Data Analytics with Python Qualifi Level 3 Extended Diploma in IT and Data Science
Data Management Software Skills AIM Qualifications Entry 3 Diploma in Skills for Working in Business, Administration and Customer Service Industries
Data Analytics/Big Data Pearson BTEC Level 3 Diploma in Engineering and Manufacturing Support Technologies
Data Engineering and Big Data Handling NOCN Level 3 VRQ in 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
Automation Fluency building AI into how work runs
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Your Plan Illustration

Built for Data Engineer

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

  1. Data Architecture NOCN · covers 8 of 10 standards Level 4
  2. Data Analytics Pearson Education Ltd · covers 3 of 10 standards Level 4
  3. Data Analytics with Python Qualifi Ltd · covers 2 of 10 standards Level 3
  4. Data Management Software Skills AIM Qualifications · covers 2 of 10 standards Entry Level
  5. Data Analytics/Big Data Pearson Education Ltd · covers 2 of 10 standards Level 3
  6. Data Engineering and Big Data Handling NOCN · covers 1 of 10 standards Level 3
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 Data Engineer?

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

  • Prompt Engineering & LLM Integration
  • Advanced Streaming Concepts (Flink/Spark Streaming)
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 Data Engineer plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

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

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3What this role really is

A data engineer builds and looks after the pipelines that move and shape data. The work makes sure the right data reaches the right place, in the right format, at the right time. It involves the nuts and bolts of data infrastructure, working directly with data to keep it clean and usable. Decisions across a business depend on this work being done well.

4What you'd actually use

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

SQL (Advanced) Advanced

Writing complex queries, optimising existing ones, building dbt models, and performing ad-hoc data analysis for debugging.

Python (pandas, NumPy) Advanced

Developing data ingestion scripts, custom transformations, Airflow DAGs, and data quality checks.

Cloud Data Platforms (AWS Redshift, GCP BigQuery, Snowflake, Databricks) Intermediate

Querying data, loading/unloading files, executing jobs, and monitoring performance within one or more of these environments.

Apache Airflow Intermediate

Developing, debugging, and monitoring DAGs (Directed Acyclic Graphs) for orchestrating data pipelines.

dbt Core/Cloud Intermediate

Building, testing, and documenting data models using SQL and Jinja, and managing dbt projects.

Git & GitHub/GitLab Intermediate

Version control for all your code, collaborating with teammates, and managing pull requests.

Data Governance & Catalog Tools (e.g., Collibra, Alation) Basic

Using the data catalogue to find and understand existing data assets, and annotating datasets you own.

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
Pipeline Design & Implementation Follows detailed instructions and templates from senior engineers. Independently designs and implements pipelines for specific data domains, adhering to architectural patterns. Consults on novel approaches. Designs complex, cross-domain pipelines and architectural patterns. Mentors others on best practices.
Technology Selection (within stack) Uses specified tools and libraries without deviation. Chooses appropriate tools and libraries from the approved tech stack for specific tasks. Proposes new tools for evaluation. Evaluates and recommends new technologies for the team's tech stack. Defines best practices for tool usage.
Incident Resolution Escalates all but the most basic, well-documented incidents to a senior engineer. Diagnoses and resolves routine pipeline incidents independently. Escalates complex or high-impact issues after initial investigation. Leads incident response for critical issues. Designs preventative measures and post-mortems.
Data Model Changes Implements changes to existing data models under direct supervision. Proposes and implements changes to existing data models for owned domains, ensuring backward compatibility. Consults with analysts on impact. Designs new data models for complex business areas. Approves significant changes to core data models.

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.

Pipeline Success Rate
The percentage of your owned data pipeline runs that complete without errors or manual intervention.
Target · >99.5%

If your pipeline runs 100 times in a month, we'd expect no more than one failure that requires your direct attention.

Data Latency for Key Domains
How quickly fresh data from source systems makes it into our 'Gold' layer (business-ready data) for your assigned domains.
Target · Within 1 hour for critical dashboards; 4 hours for daily reports

A new transaction hits our sales system at 10:00 AM. By 10:45 AM, it should be reflected in the sales dashboard that uses your pipeline.

Ticket Resolution Time
The average time it takes you to resolve data-related incidents or bugs reported by downstream users.
Target · <24 hours for P1/P2 incidents; <3 days for P3/P4

An analyst reports a data discrepancy in a sales report. You should identify and fix the root cause within a day, or at least provide a clear workaround.

Code Quality & Maintainability
Regular assessment of your code for readability, adherence to standards, and ease of future modifications.
Target · Fewer than 5 major bugs or critical code review comments per quarter

Your dbt models are easy for another engineer to understand and extend, and they pass all automated data quality checks without issue.

Data Accuracy & Reliability
How much our internal teams trust the data you're providing. This is about the perceived correctness of the data.
  • Fewer questions about data correctness from analysts
  • positive feedback from business users
  • minimal discrepancies found in ad-hoc checks.
Documentation Clarity & Completeness
How well your data models, pipelines, and transformation logic are documented, making it easy for others to understand and use.
  • New team members can quickly understand your work
  • fewer follow-up questions from analysts about data definitions
  • all key datasets have clear descriptions in our data catalogue.
Proactive Issue Identification
Your ability to spot potential data problems (e.g., schema drift, upstream changes) before they cause major pipeline failures.
  • You flag potential issues in stand-ups
  • you've set up monitoring that catches problems early
  • you propose solutions to prevent future incidents.
Collaboration & Peer Feedback
How effectively you work with other engineers, product managers, and analysts, and how well you give and receive constructive feedback.
  • Positive comments in 360-degree reviews
  • active participation in technical discussions
  • willingness to help unblock teammates
  • clear communication on project blockers.

7Would you like it

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

What people enjoy
Building things that work

You get a real kick out of seeing your dbt models run successfully, knowing the data is flowing cleanly. There's satisfaction in deploying a new pipeline and watching it deliver reliable data to users.

Completing a complex data transformation that reliably feeds a critical business dashboard, and seeing that dashboard used daily.

Solving complex puzzles

Debugging a tricky data quality issue or optimising a slow-running query is your idea of a good challenge. You enjoy the process of dissecting a problem and finding an elegant solution.

Identifying the root cause of a subtle data discrepancy that had been baffling the analytics team for weeks, and implementing a fix.

Seeing the impact of your work

You like knowing that the data you're preparing is directly enabling important business decisions. You're motivated by the connection between your code and real-world outcomes.

Building a new data set that allows the marketing team to launch a highly targeted campaign, leading to a measurable increase in customer engagement.

What frustrates people
  • Unclear or constantly changing data requirements from business teams.
  • Dealing with 'schema drift' from upstream source systems that break your pipelines without warning.
  • Being blocked by other teams for access or information, slowing down your progress.
  • The constant tension between building the 'right' way and the 'fast' way.
  • Legacy data sources that are difficult to integrate or unreliable.
What this role does not give you
  • A perfectly predictable, routine workday (expect some urgent issues to pop up).
  • Complete autonomy on architectural decisions (you'll work within established patterns).
  • A role solely focused on greenfield projects (there's always maintenance and improvement of existing pipelines).
  • A 'hands-off' management role (you'll be writing code and debugging daily).

8Who you work with

Your work directly impacts the reliability and accuracy of all data-driven insights. If our pipelines are robust, business teams can make informed decisions quickly. If they're not, we risk making poor choices, wasting marketing spend, or misallocating resources. You're essentially building the foundational plumbing for our entire data ecosystem, making sure the water flows cleanly and efficiently to everyone who needs it.

Inside the business
  • Product Managers (for data requirements)
  • Analytics Team (your primary users)
  • Software Engineering Teams (source system owners)
  • Data Scientists (for model training data)
  • Regional Business Teams (who use your data for reporting)
Outside the business
  • Cloud Platform Vendors (e.g., AWS, Snowflake support)
  • Data Tool Providers (e.g., dbt Labs support)

9What you need before you start

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

  • 2-5 years of hands-on experience as a Data Engineer or in a similar role, building and maintaining production-grade data pipelines.
  • Proven ability to write complex, performant SQL queries and robust Python scripts for data manipulation.
  • Experience with at least one major cloud data platform (AWS, GCP, or Azure) and a cloud data warehouse (Snowflake or Databricks).
  • Solid understanding of data warehousing concepts, including dimensional modelling.
  • Experience with an orchestration tool like Apache Airflow for scheduling and monitoring jobs.
  • Familiarity with version control systems, particularly Git.

10What to practise next

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

Cloud FinOps Deep Dive

Important within 12 months. Cloud costs are a significant spend for us. Moving beyond basic awareness, you'll need to understand how your architectural choices directly translate into pounds and pence, and how to actively contribute to cost optimisation.

Cost Allocation & Tagging · Compute Optimisation · Storage Tiering · Reserved Instances & Savings Plans · Cost Anomaly Detection

  • This week: Review our current cloud billing dashboards (e.g., AWS Cost Explorer, Snowflake usage).
  • This month: Identify one pipeline you own and propose a small cost-saving optimisation (e.g., reducing warehouse size during off-peak hours).
  • Month 2: Take an online course or read a book on FinOps principles.
  • Month 3: Present a summary of your cost-saving findings and recommendations to the team.

Quick win: Simply start looking at the cloud cost associated with your pipelines. Awareness is the first step to optimisation.

11Staying current once you are in

What people here do to keep up
  • Regularly participate in data engineering meet-ups or online communities (e.g., Data Engineering Weekly, dbt Slack community).
  • Contribute to open-source data projects or build personal projects to experiment with new technologies.
  • Take online courses or attend workshops on advanced SQL, Python for data, or new cloud data services.
  • Read industry blogs and research papers to stay informed about trends and best practices in data engineering.
  • Attend relevant industry conferences (e.g., Data + AI Summit, Fivetran Modern Data Stack Conference) when budget allows.

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.

Prompt Engineering & LLM Integration

Critical within 6 months—this isn't future-gazing; it's happening now. Analysts and engineers who figure out how to effectively use Large Language Models (LLMs) to generate code, summarise data, or draft documentation will significantly outproduce their peers. It's a game-changer for productivity.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG (Retrieval Augmented Generation)
  • Output Validation & Hallucination Detection
  • Prompt Chaining

Advanced Streaming Concepts (Flink/Spark Streaming)

Important within 12-18 months. As our business demands more real-time insights (e.g., fraud detection, live personalisation), our reliance on batch processing will decrease. Understanding and building real-time data pipelines will become a core part of the role.

  • Event-Driven Architectures
  • Stateful Stream Processing
  • Windowing Functions
  • Exactly-Once Semantics
  • Backpressure Management

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Dimensional Modelling
  • DataOps & CI/CD for Data
  • Distributed Systems Design (Concepts)
  • Cloud FinOps (Cost Awareness)
  • Data Mesh Architecture (Understanding)

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

    Junior Data Engineer

    1-2 years

    Skills to master

    • Mastering SQL, foundational Python scripting, understanding basic data warehousing concepts, and becoming proficient with our core ETL/ELT tools (Airflow, dbt). Learning to debug and resolve simple pipeline issues.

    You're ready to move on when

    • Consistently delivers well-tested code for assigned tasks.
    • Can independently resolve common pipeline failures.
    • Actively contributes to code reviews and team discussions.
    • Demonstrates a solid grasp of our core data models and architecture.
  2. 2

    Software Engineer (with data interest)

    2-3 years (transition time)

    Skills to master

    • Translating strong software development skills into data engineering context: learning distributed systems concepts, mastering SQL for data transformation, understanding data quality principles, and becoming proficient with data orchestration tools. Focusing on data reliability.

    You're ready to move on when

    • Has built robust, production-grade applications and understands software best practices.
    • Demonstrates a strong interest in data and has actively worked with databases or data processing in previous roles.
    • Quickly picks up new data-specific tools and methodologies.
    • Can apply software engineering principles (e.g., testing, CI/CD) to data pipelines.
  3. 3

    Data Analyst (up-skilling)

    2-4 years (transition time)

    Skills to master

    • Moving beyond querying to building and maintaining data infrastructure. This means deep-diving into Python programming, learning cloud data platforms, mastering data modelling for performance, and understanding data pipeline orchestration. A strong focus on automation and scalability.

    You're ready to move on when

    • Has a deep understanding of business data needs and how data is used for insights.
    • Has strong SQL skills and experience with data visualisation tools.
    • Has independently started building personal data projects or automating analytical tasks.
    • Shows a clear drive to move into infrastructure and engineering roles.

15Where this role leads

The long view: This work can lead to a role as a technical specialist or into managing a team of engineers.

16Pay and demand

ONS's coding index maps “Data Engineer” to more than one occupation, so there is no one median to quote. Rather than pick, here is each one it could be, with its own figure:

  • IT business analysts, architects and systems designers £60,288 a year
  • Telecoms and related network installers and repairers £39,998 a year

ONS Annual Survey of Hours and Earnings, from the April 2025 survey, about six months old when published, as ASHE always is, under the Open Government Licence.

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 Data Engineer is actually changing.

Where it leads

Roles this leads to

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 Architecture, worked against Data Engineer.

Data Architecture Level 4

Applied to your work in Data Engineer

The objective of this unit is to provide learners with a comprehensive understanding of data architecture principles, including data architecture patterns, metadata management, and data governance. Learners will also explore the concepts of IoT and streaming data management, big data platforms, and cloud platforms for data storage and processing.

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.

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See Your Progress Grow Illustration
Data Engineer
  • Dimensional Modelling
  • DataOps & CI/CD for Data
  • Distributed Systems Design (Concepts)
  • Cloud FinOps (Cost Awareness)
  • Data Mesh Architecture (Understanding)
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.

17The 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

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

  1. Senior Data Engineer

    3-5 years

    Level 3 (Senior)

    • Advanced Data Modelling Designing complex data models for new business areas.
    • Performance Optimisation Deep expertise in tuning cloud data warehouses and pipelines for cost and speed.
    • Data Governance Implementation Working with data stewards to implement data lineage and quality frameworks.
    • Mentorship Actively coaching and developing junior team members.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, data engineering can involve a fair bit of repetitive work. But what if you could offload some of that to AI? We're not talking about replacing you, but giving you a powerful co-pilot that helps you get more done, faster, and with less effort. Think of it as having an extra pair of hands, or even a brain, that handles the grunt work.

In this role, you'll find AI isn't just a buzzword; it's a practical tool that can genuinely make your life easier. We're actively exploring and integrating AI into our data engineering workflows, and we expect you to get stuck in. This means using AI to automate the boring bits, giving you more time to focus on the interesting, complex problems that truly need your human intelligence.

Automated Code & Test Generation

Imagine writing less boilerplate code. You'll use tools like GitHub Copilot to suggest SQL queries, Python functions, and even dbt models as you type. Plus, AI can help you automatically generate data quality tests based on your table schemas, catching potential issues much earlier. It's like having a coding assistant who knows our codebase.

Intelligent Data Observability

Instead of manually trawling through logs, you'll use AI-powered observability platforms (like Monte Carlo) that automatically detect anomalies in data quality, schema changes, or freshness issues. This means you'll get alerted to problems before our business users even notice, drastically reducing your firefighting time and improving data trust. It's about being proactive, not reactive.

Smarter Documentation & Translation

No one loves writing documentation, but it's essential. You can use LLMs to draft initial versions of technical documentation for your pipelines and data models. Need to explain a complex concept to a regional team in their native language? AI can help with quick, accurate translations, ensuring everyone's on the same page, no matter where they are.

AI-Assisted Incident Communication

When a pipeline goes down, clear and fast communication is key. AI can help you rapidly summarise complex technical incident details from Slack channels and logs into a concise, non-technical executive summary. This frees you up to focus on the fix, while AI handles the initial drafting of stakeholder updates. It's about getting the message out quickly and accurately.

Common questions

Common questions

How do you become a Data Engineer?

Common routes in include Junior Data Engineer (1-2 years), Software Engineer (with data interest) (2-3 years (transition time)) and Data Analyst (up-skilling) (2-4 years (transition time)). Times vary with prior experience.

Where can a Data Engineer progress to?

This role can lead on to Senior Data Engineer (3-5 years), depending on the skills you build.

What new skills matter most for a Data Engineer?

Increasingly, Prompt Engineering & LLM Integration and Advanced Streaming Concepts (Flink/Spark Streaming). 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

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18Where to go from here

Other roles at Level 3

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 involved transfer across most industries. Any organisation that handles data needs solid data infrastructure, so this work turns up in e-commerce, finance, healthcare, media and elsewhere. Experience with cloud-native and distributed systems is especially useful.

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 Data Engineer plan, free 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.