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.

Start my Data Engineer plan, free

Free to start: a plan built for this role, and a Future Fluency module to try first. No card.

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Where should we send it?

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 Staff Data Engineer
UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Data Pipeline Engineer · ETL Developer · Cloud Data 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 9 of 17 standards

Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 2 of 17 standards

AIM Qualifications Entry 3 Diploma in Skills for Working in Business, Administration and Customer Service Industries AIM Qualifications · Entry Level · covers 2 of 17 standards

Pearson BTEC Level 3 Diploma in Engineering and Manufacturing Support Technologies Pearson Education Ltd · Level 3 · covers 2 of 17 standards

NOCN Level 3 VRQ in Data Science NOCN · Level 3 · covers 1 of 17 standards

Qualifi Level 3 Diploma in Data Science Qualifi Ltd · Level 3 · covers 1 of 17 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 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

AI Governance Fluency using AI responsibly

Start my Data Engineer plan, free

Your Plan Illustration

Built for Data Engineer

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

Data Architecture NOCN · covers 9 of 17 standards Level 4
Data Analytics with Python Qualifi Ltd · covers 2 of 17 standards Level 3
Data Management Software Skills AIM Qualifications · covers 2 of 17 standards Entry Level
Data Analytics/Big Data Pearson Education Ltd · covers 2 of 17 standards Level 3
Data Engineering and Big Data Handling NOCN · covers 1 of 17 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
Data Mesh Principles

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

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.

Start my Data Engineer plan, free Ready to pay? Subscribe for £70 a month

3What this role really is

A data engineer builds and looks after the systems that move data around a business. The work makes sure the right data reaches the right people at the right time, so they can make good decisions. It's hands-on, with code that has a visible effect day to day.

4What you'd actually use

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

AWS (S3, EMR/Glue, Redshift/Athena) Intermediate

You'll be navigating S3 buckets for data storage, running jobs on existing EMR/Glue clusters, and querying Redshift/Athena for data analysis. You'll also need to understand existing IAM policies.

Snowflake/Databricks Intermediate

You'll be writing complex SQL queries against existing tables, running notebooks for data transformations, and understanding how virtual warehouses work to manage compute resources.

Apache Airflow Intermediate

You'll be monitoring existing DAGs (Directed Acyclic Graphs), triggering manual runs when needed, and debugging simple task failures. You'll also start writing basic tasks within existing DAGs.

dbt (data build tool) Intermediate

You'll be creating and modifying models within our existing dbt projects, writing data tests (like `not_null` and `unique`), and ensuring our data transformations are well-structured and documented.

Apache Kafka Basic

You'll need to understand the basic concepts of topics, producers, and consumers. You might use tools to inspect messages on a topic to troubleshoot streaming data issues.

Terraform Basic

You'll be reading and understanding existing Terraform configurations for our infrastructure. You might make minor changes to variables to adjust resources, but you won't be writing modules from scratch just yet.

Python (pandas, boto3) Intermediate

You'll be writing scripts using `pandas` for more complex data manipulation and using `boto3` to interact with AWS services based on existing patterns for things like moving files or triggering jobs.

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
Pipeline Design for New Data Source	Proposes initial design options, seeks detailed guidance and approval from Senior Engineer.	Designs end-to-end pipeline, including data modelling and transformation logic. Seeks review and approval from Senior Engineer for major architectural choices.	Architects the entire solution, considering scalability, cost, and future-proofing. Approves design and guides implementation.
Troubleshooting a Production Pipeline Failure	Identifies the error message, gathers initial logs, and escalates to Senior Engineer for resolution.	Independently investigates, diagnoses the root cause, and implements a fix. Informs manager of the issue and resolution.	Leads the incident response, coordinates across teams if needed, and designs preventative measures to avoid recurrence.
Tool Selection for a New Use Case	Researches options based on specific criteria provided by manager, presents findings.	Evaluates potential tools against requirements, performs proof-of-concept, and recommends a solution to manager.	Defines the evaluation criteria, makes the final tool selection, and outlines the integration strategy.
Data Model Changes for a Core Dataset	Suggests minor changes to existing models, requires full review and approval.	Designs and implements changes to existing data models, including impact analysis and testing. Seeks peer review and manager approval.	Approves significant data model changes, ensures alignment with overall data strategy, and manages communication to downstream consumers.

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.

Data Freshness SLA Adherence

This is about how often your pipelines deliver data within the expected timeframe. If a dashboard needs data by 8 AM, we need it by 8 AM.

Target · 99.5% of critical datasets delivered on time

If a daily sales report is due by 9 AM, and your pipeline consistently gets it there by 8:30 AM, you're hitting it. If it's late once a month, that's fine, but more than that, and we've got a problem.

Pipeline Uptime & Stability

How often do your core ingestion and transformation jobs run without failing? We want things to just work, most of the time.

Target · 99.9% uptime for core ingestion jobs

Your main customer data pipeline runs 30 times a month. If it fails once, that's 96.7% uptime. We're aiming for fewer than one failure every few months on critical pipelines.

Ticket Resolution Time (P2/P3)

When something goes wrong or an analyst needs help, how quickly do you jump on it and get it sorted? We're talking about non-critical issues here, not 'the whole system is down' emergencies.

Target · P2 bugs resolved within 48 hours; P3 requests within 5 working days

An analyst flags a minor data discrepancy on Monday morning. You investigate, identify the fix, and deploy it by Wednesday afternoon. That's hitting the target.

Data Quality Incident Rate

This measures how often bad data slips through your pipelines into our downstream systems. Ideally, it should be almost never.

Target · Fewer than 2 critical data quality incidents per quarter

A critical incident might be a sales figure being off by 10% for a whole week due to a pipeline error. We want to avoid those big, embarrassing mistakes.

Proactive Problem Identification

You're not just fixing what breaks; you're spotting potential issues before they become real problems. This means looking at logs, monitoring dashboards, and just having a good feel for the data.

You flag an upstream schema change before it breaks your pipeline. You notice a data volume anomaly and investigate it without being asked. You propose improvements to existing pipelines based on observed inefficiencies.

Effective Collaboration & Communication

Can you explain complex technical issues to non-technical folks? Do you work well with data analysts and software engineers? It's about being a good team player, honestly.

Data analysts consistently praise your clarity in explaining data issues. You actively participate in code reviews, offering constructive feedback. You keep stakeholders informed about pipeline status and any delays, even if it's just a quick message.

Code Quality & Maintainability

Your code isn't just functional; it's clean, well-documented, and easy for others to understand and pick up. Future-you (or another engineer) will thank you.

Your pull requests consistently pass code reviews with minimal suggestions for improvement. New team members can understand and modify your pipelines quickly. Your documentation is actually useful, not just a tick-box exercise.

Ownership & Accountability

When you're given a task or a pipeline, you take it seriously. You see it through, you ask for help when you need it, and you don't drop the ball.

You consistently meet your commitments. When a pipeline you own breaks, you're the first one on it, even if it's outside normal hours. You follow up on issues until they're truly resolved, not just patched.

7Would you like it

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

What people enjoy

Solving Complex Puzzles

You get a real kick out of debugging a tricky pipeline failure or figuring out how to transform messy, disparate data into a clean, unified dataset. The 'aha!' moment is what you live for.

Spending an afternoon wrestling with a particularly stubborn `NullPointerException` in a Spark job, and finally figuring out the exact data type mismatch that caused it. Pure satisfaction.

Building Reliable Systems

You're driven by the desire to build robust, scalable data infrastructure that just works. Seeing your pipelines hum along smoothly, delivering fresh data day after day, is genuinely rewarding.

Successfully deploying a new data ingestion pipeline that handles millions of records daily without a single hiccup for months, knowing it's feeding critical business dashboards.

Learning & Growing

The data world changes fast, and you're excited by that. You're always keen to pick up new tools, learn new cloud services, or dive into a different data modelling technique. Stagnation is your enemy.

Taking the initiative to learn dbt on your own time, then proposing its use for a new project because you see how it could improve our data transformation workflow.

What frustrates people

The Upstream Surprise: A source application team changes an API endpoint or a database column with zero notice, causing your production pipeline to fail spectacularly at 3 AM.
The Analyst's 'Query of Death': Discovering that the entire data warehouse is grinding to a halt because an analyst connected a BI tool and is running a query with five cross-joins on multi-billion row tables.
Legacy System Archaeology: Being asked to ingest data from a 20-year-old AS/400 system that exports malformed CSV files and for which the only documentation is 'the guy who built it, who left 10 years ago.'
'Just give me the data': Receiving a ticket from a stakeholder with a vague, one-line request that requires weeks of discovery, data modelling, and engineering work, which they expect to be done by tomorrow.
Debugging in the Dark: A complex Spark job runs for four hours on the full production dataset and then fails with a cryptic `NullPointerException`, forcing you to start the entire process over again.
The 'Data Plumber' Perception: Spending weeks building a robust, scalable, and well-tested data platform, only to be viewed by other departments as someone who just 'moves data from A to B.'
Garbage In, Gospel Out: Being held accountable for the accuracy of a dashboard when you know the source data you're being fed is inconsistent, incomplete, and fundamentally flawed, but you're politically unable to force the source team to fix it.

What this role does not give you

A perfectly clean, well-documented data landscape from day one – you'll be helping to build it.
A role where you're always building shiny new things; a good chunk of your time will be maintaining and improving existing systems.
A job where you're completely isolated from other teams; you'll be talking to a lot of different people.
A 9-to-5 job where nothing ever goes wrong; pipelines break, sometimes at inconvenient times.

8Who you work with

Your work directly underpins almost every data-driven decision we make. If our data pipelines aren't robust, accurate, and timely, the entire company's ability to understand its performance and make strategic moves is compromised. You're essentially building the circulatory system for our business intelligence.

Inside the business

Data Analysts (they'll use your pipelines for their dashboards)
Product Managers (they need data to understand user behaviour)
Software Engineers (you'll work with them to get data from their applications)
Machine Learning Engineers (they need clean, reliable data for their models)
Operations Team (sometimes they need specific data for their processes)

Outside the business

None directly, but you might interact with vendor support for our data tools.

9What you need before you start

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

At least 2 years of hands-on experience building and maintaining data pipelines in a production environment.
Proven ability to write robust, performant SQL queries for data transformation.
Strong programming skills in Python, including experience with data manipulation libraries like pandas.
Experience with at least one cloud data platform (AWS, Azure, or GCP), with a preference for AWS.
Familiarity with version control systems, particularly Git.
A good understanding of data warehousing concepts and dimensional modelling.
Ability to independently troubleshoot and resolve data pipeline issues.

10What to practise next

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

Advanced Cloud Data Platform Optimisation (AWS)

As our data volumes and complexity grow, simply using cloud services isn't enough. You'll need to become an expert at squeezing every bit of performance out of them while keeping costs in check. This means understanding the nuances of each service.

Redshift/Snowflake Cost Optimisation · Glue/Spark Performance Tuning · Serverless Data Pipelines (Lambda, Step Functions) · Advanced IAM Policies

This quarter: Take an advanced AWS certification focused on data (e.g., Data Analytics - Specialty).
Next quarter: Lead a project to optimise the cost or performance of an existing critical data pipeline.
Month 6: Experiment with building a small, entirely serverless data pipeline for a specific use case.
Month 9: Proactively identify and fix a significant cost inefficiency in our current cloud data setup.

Quick win: Review the AWS Cost Explorer for our data services this week and identify the top 3 cost drivers. Start asking 'why?'

Real-time Data Processing & Streaming

More and more business decisions need to be made in near real-time. Moving beyond batch processing to truly understand and implement streaming architectures will become increasingly vital.

Kafka Streams/ksqlDB · Schema Registry & Evolution · Event-Driven Architecture · Stream Processing Frameworks (e.g., Flink, Spark Streaming)

This quarter: Take an online course on Apache Kafka Streams or ksqlDB.
Next quarter: Contribute to a project that involves real-time data ingestion or processing.
Month 6: Design a small proof-of-concept for a real-time data product, even if it's just a simple alert system.
Month 9: Become the team's go-to person for questions about streaming data and event-driven patterns.

Quick win: Set up a local Kafka instance and experiment with producing and consuming messages. It's easier than you think to get started.

11Staying current once you are in

What people here do to keep up

Actively participate in online data engineering communities (e.g., Data Engineering Weekly, Reddit's r/dataengineering).
Contribute to open-source data projects, even small bug fixes or documentation improvements.
Attend virtual or in-person data conferences and meetups to stay current and network.
Complete online courses or specialisations on platforms like Coursera, Udemy, or DataCamp in areas like advanced Spark, Kafka, or specific cloud data services.
Set up a personal data project (e.g., building a small data pipeline for your favourite sports league stats) to experiment with new tools and techniques.

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

Honestly, competitors are already using large language models (LLMs) to draft reports in 10 minutes that used to take 2 hours. Data engineers who figure this out will outproduce their peers significantly. This isn't future-gazing; it's happening now.

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

Data Mesh Principles

As our data landscape grows, centralising everything can become a bottleneck. Data Mesh offers a way to scale data ownership and delivery, treating data as a product. Understanding this will be key for future architectural decisions.

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

13What you’ll use

What you’ll use

Skills this role draws on

Technical

ETL/ELT Design Patterns
Dimensional Data Modelling
Distributed Computing Principles
CI/CD for Data Pipelines
Data Observability

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
Junior Data Engineer / Associate Data Engineer
1-2 years
Skills to master
- Mastering SQL and Python for data manipulation, understanding basic cloud services (S3, EC2), learning our internal data models and pipeline frameworks (like dbt and Airflow).
You're ready to move on when
- Can independently fix minor pipeline issues with minimal supervision.
- Consistently delivers clean, well-tested code for assigned tasks.
- Understands the full lifecycle of a simple data pipeline from ingestion to consumption.
- Actively participates in code reviews and asks thoughtful questions.
2
Software Developer (with data interest)
2-3 years of transition
Skills to master
- Shifting from application development to data-centric programming (Spark, dbt), learning data modelling principles, understanding distributed systems, and getting to grips with cloud data platforms.
You're ready to move on when
- Has built personal projects involving data ingestion and transformation.
- Demonstrates strong SQL skills alongside programming expertise.
- Expresses a genuine interest in data integrity, scalability, and performance.
- Has taken relevant online courses or certifications in data engineering fundamentals.
3
Data Analyst (moving into engineering)
1-3 years of transition
Skills to master
- Deepening programming skills (especially Python), learning infrastructure as code (Terraform), understanding pipeline orchestration (Airflow), and moving from consuming data to building the underlying infrastructure.
You're ready to move on when
- Has identified and automated repetitive data tasks in their previous role.
- Has a strong understanding of data quality issues from an analyst's perspective.
- Can write complex SQL queries and has started experimenting with Python scripting for data tasks.
- Shows initiative in understanding how data gets into the warehouse.

15Where this role leads

The long view: This role can lead towards deep technical specialism, into managing a team, or into broader data architecture and governance work.

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.

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 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.

Start my Data Engineer plan, free

See Your Progress Grow Illustration

Data Engineer

ETL/ELT Design Patterns
Dimensional Data Modelling
Distributed Computing Principles
CI/CD for Data Pipelines
Data Observability

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.

Senior Data Engineer
3-5 years in current role

Level 3 (OFQUAL 6-7)
- Advanced Cloud Architecture Designing multi-service data solutions on AWS (e.g., serverless pipelines, advanced security).
- Complex Data Modelling Designing and implementing enterprise-wide data models (e.g., data vault, advanced dimensional models).
- Performance Optimisation Deep expertise in optimising Spark, Snowflake, and other big data technologies.
- Data Governance Implementation Leading efforts to implement data quality, lineage, and metadata management frameworks.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, data engineering can be a grind. The repetitive tasks, the endless debugging, the mountain of documentation. But what if you could offload a significant chunk of that to an intelligent assistant? Our team is already seeing huge gains, and we want you to be part of it.

We're not talking about replacing you; we're talking about giving you superpowers. AI tools are becoming incredibly good at handling the mundane, the boilerplate, and even helping you think through complex problems. This isn't just a 'nice-to-have' anymore; it's how top-tier data engineers get more done and focus on the really interesting challenges.

Boilerplate Code Generation

Use AI assistants like GitHub Copilot to automatically generate standard Python code for API ingestion, boilerplate for dbt models, or Terraform configurations for common AWS resources. It's like having a coding partner who never sleeps.

Query Optimisation & Debugging

Got a slow-running SQL query or a cryptic Spark error message? Paste it into an AI chat model and ask for optimisation suggestions, potential root causes, or a plain-English explanation of the error. It's often quicker than searching Stack Overflow.

Documentation Automation

Use AI tools to automatically scan database schemas and generate first-draft documentation for data dictionaries. Or, ask it to summarise the logic of a complex Airflow DAG in plain English. Less writing, more building.

New Tech Research & Learning

Need to quickly compare two emerging data technologies (e.g., 'Compare DuckDB and Polars for local data processing') or get a starter guide for a new cloud service? AI can give you a solid head start, saving you hours of research.

Common questions

How do you become a Data Engineer?

Common routes in include Junior Data Engineer / Associate Data Engineer (1-2 years), Software Developer (with data interest) (2-3 years of transition) and Data Analyst (moving into engineering) (1-3 years of transition). Times vary with prior experience.

Where can a Data Engineer progress to?

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

What new skills matter most for a Data Engineer?

Increasingly, Prompt Engineering & LLM Integration and Data Mesh Principles. 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 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.

Data Engineer

Where should we send it?

Two short quizzes about you

Will AI change your work as a Data Engineer?

What's rising in its place

Prompt Engineering & LLM Integration

Data Mesh Principles

Skills this role draws on

Technical

How you actually get there, here

Junior Data Engineer / Associate Data Engineer

Software Developer (with data interest)

Data Analyst (moving into engineering)

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

Senior Data Engineer

Where AI is starting to help

Boilerplate Code Generation

Query Optimisation & Debugging

Documentation Automation

New Tech Research & Learning

Common questions

Want a read on where you stand?