United Kingdom · Technical roles · Entry Level (0-2 years)

Analytics Engineer I

Here is the whole job, in plain words. What it is, a real day, 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.

Experience bandEntry Level (0-2 years)
Direct reportsNo direct reports
Reports toSenior Analytics Engineer
UK framework levelUsually someone starting out, or keeping a process running

Also advertised as Junior Analytics Engineer · Entry-Level Data Modeller · Data Pipeline Support Analyst

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

Start with a free Future Fluency check, tuned to Analytics Engineer I

Ten quick questions, one per Future Fluency, asked against this role rather than a generic one. About five minutes, and no card.

Start the check, free

1What this role really is

This isn't about just running reports; it's about building the pipes and structures that make those reports possible. You'll be the person laying the groundwork, making sure the data flows cleanly from its source right through to the dashboards our business teams use every day. Think of it as being the apprentice plumber for our data ecosystem—you'll learn the trade, get your hands dirty, and help keep everything running smoothly. It's a foundational role, perfect if you're keen to get into the nuts and bolts of data architecture.

2What you'd actually use

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

Snowflake or Databricks SQLIntermediate

Writing and executing SQL queries for data transformation, exploration, and ad-hoc analysis within our cloud data warehouse.

dbt Core (data build tool)Intermediate

Building and maintaining data models, writing data quality tests, and running dbt commands to transform data in our warehouse, following existing patterns.

Fivetran or AirbyteBasic

Monitoring pre-built data connectors, troubleshooting basic ingestion failures, and potentially configuring new, simple data sources under supervision.

Tableau or LookerIntermediate

Building and modifying dashboards and reports from pre-defined data sources, applying basic visualisation best practices for business users.

Collibra or AlationBasic

Using our data catalog to find and understand existing data assets, and helping to annotate data definitions based on input from subject matter experts.

Apache AirflowBasic

Monitoring and triggering existing data pipelines (DAGs), understanding basic task dependencies, and troubleshooting simple run failures.

3What 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 Model Changes	Escalate all proposed changes to your Senior Analytics Engineer for review and approval. You'll implement changes only after explicit sign-off.	Propose minor schema changes for specific data marts, with manager review. Major changes still require senior architect input.	Design and approve data model changes for entire business domains (e.g., Marketing). Consult Lead Architect on cross-domain impacts.
Tool/Technology Selection	No independent authority. You'll use the tools provided and learn how they work. If you have suggestions, share them with your manager.	Suggest specific features or libraries within existing tools. New tool adoption requires manager and team lead approval.	Recommend and evaluate new tools for specific project needs. Lead proof-of-concepts for potential new additions to the stack, with Director input.
Project Prioritisation	Your tasks are prioritised by your manager. If you have conflicting priorities, you'll raise them immediately to your manager for resolution.	Prioritise your own tasks within a project, aligning with the project lead. Escalate conflicts to your manager.	Influence project prioritisation for your workstream, making recommendations to the Director based on technical feasibility and business impact.
Budget Allocation	No budget authority. You'll ensure your work adheres to existing resource constraints (e.g., cloud compute).	Recommend resource allocation for specific project components (e.g., larger Snowflake warehouse for a specific job), with manager approval for costs over £1K.	Manage project-specific budgets up to £5K. Make recommendations for larger capital expenditures to the Director.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Data Pipeline Uptime

The percentage of time your assigned data pipelines run successfully without errors.

Target · Maintain a 99.5% success rate on assigned data pipelines.

If you're responsible for 10 pipelines, and only 1 fails for an hour in a month (720 hours), that's a 99.86% uptime. We're looking for consistent reliability on your patch.

Task Turnaround Time

How quickly you complete assigned tasks, measured against the estimated effort.

Target · Close 85% of assigned Jira tickets within the estimated story points or agreed deadline.

If a task is estimated at 3 story points and you complete it within that timeframe, that counts as a success. It's about realistic estimation and consistent delivery.

Data Quality Incidents

The number of data quality bugs or errors introduced into production by your work.

Target · Introduce fewer than 5 data quality bugs per quarter.

You push a dbt model that accidentally changes a customer ID from a number to text, breaking downstream reports. That's one incident. We expect some, especially as you learn, but we're looking for a downward trend.

Adherence to Standards

How well you follow our team's coding standards, documentation guidelines, and best practices for data modelling.

Your code reviews show consistent application of our style guide. Your dbt models are well-organised and easy to understand. You use our naming conventions without needing constant reminders. You're not cutting corners, even when under a bit of pressure.

Learning & Growth Pace

How quickly you pick up new tools, concepts, and methodologies, and apply feedback.

You ask thoughtful questions, demonstrate understanding of new concepts in subsequent tasks, and actively seek out learning resources. You don't make the same mistake twice after receiving feedback. You're starting to contribute ideas in team discussions, even if they're small.

Proactive Communication

Keeping your manager and team informed about your progress, blockers, and any potential issues.

You flag issues early, before they become big problems. You provide clear updates in daily stand-ups. You're not afraid to ask for help when you're stuck, rather than spinning your wheels for hours.

Team Collaboration

How effectively you work with your immediate team and other data professionals.

You're responsive to requests for help (when appropriate for your level). You actively participate in team meetings. You share what you've learned with others. You're a good listener and respectful of different opinions.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy

Building & Creating

You get a real kick out of seeing a new dbt model you've built go live, or a data pipeline you've configured successfully ingest data. You enjoy the process of taking raw ingredients and turning them into something structured and useful.

Spending an afternoon wrestling with a complex SQL query, finally getting it right, and seeing the correct data appear in a dashboard brings you genuine satisfaction.

Continuous Learning

You're always keen to pick up new technical skills, whether it's a new cloud service, a different way to write SQL, or understanding a new data modelling concept. The idea of mastering a complex technical domain excites you.

You'll happily spend an hour after work playing with a new feature in dbt Cloud or trying out a new Python library for data processing.

Making an Impact (indirectly)

While you might not be presenting to the board, you understand that the clean, reliable data you help produce is essential for those who do. You feel good knowing your work is a critical building block for important business decisions.

Your manager tells you that a report you helped build was used to identify a key customer segment, and you feel a sense of pride in your contribution.

What frustrates people

Dealing with source systems that change their data structure without warning ('schema drift'), breaking your pipelines.
Spending hours trying to debug a data quality issue only to find the root cause is a manual error in an upstream operational system.
Getting vague requirements like 'just make the report show the right numbers' without any specific definition of 'right'.
The sheer amount of time spent on 'data cleaning' rather than building shiny new things.
Working on a task for days, only for it to be deprioritised or put on hold because of an 'urgent' new request.

What this role does not give you

High-level strategic decision-making (that comes later).
A perfectly clean, stable data environment (it's always a work in progress).
Guaranteed immediate deployment of every piece of work you create.
A 'set it and forget it' kind of job; data needs constant attention.

6Who you work with

This role directly supports the reliability and accuracy of our entire data ecosystem. Your work, though guided, ensures that the foundational data layers are robust enough for others to build on. Get it right, and the whole business benefits from trustworthy insights; get it wrong, and we're all flying blind, making decisions based on shaky numbers.

Inside the business

Your Senior Analytics Engineer and the wider Analytics Engineering team
Data Analysts who use the models you help build
Product Managers who need data for their features
Business Operations teams who rely on accurate metrics

Outside the business

Third-party data vendors (e.g., CRM providers, marketing platforms) – you'll mostly monitor, not directly engage

7What you need before you start

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

A solid grasp of SQL – you should be able to write complex queries without constant hand-holding.
Some exposure to a scripting language (like Python) for data manipulation, even if it's just basic scripts.
Familiarity with version control systems, specifically Git (e.g., branching, merging, pull requests).
A genuine interest in data, systems, and how things connect. You should enjoy solving puzzles and debugging.

8What to practise next

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

Real-time Data Processing Basics

Businesses increasingly need immediate insights. While most of our current data is batch-processed, understanding basic concepts of streaming data (e.g., Kafka, Flink) will become more important as we move towards real-time analytics.

Batch vs. Stream Processing · Event-Driven Architectures · Latency Considerations

This quarter: Read introductory articles on Apache Kafka or similar messaging queues to understand their purpose.
Next quarter: Look for internal projects (if any) that touch on near real-time data and try to understand their architecture.
Within 6 months: Consider a basic online course on streaming data fundamentals.

Quick win: Pay attention to any business requests for 'fresher' data. This will highlight where real-time solutions might eventually be needed.

9Staying current once you are in

What people here do to keep up

Actively participate in online data communities (e.g., dbt Slack community, local meetups).
Complete relevant online courses on platforms like DataCamp, Udemy, or Coursera (e.g., 'Advanced SQL', 'Introduction to dbt').
Contribute to open-source projects or build personal data projects to showcase your skills and curiosity.
Attend internal knowledge-sharing sessions and workshops to learn from senior colleagues.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Prompt Engineering for Data Tasks

AI assistants are becoming incredibly powerful for generating SQL, drafting documentation, and even suggesting data models. Knowing how to 'talk' to these models effectively—how to craft the right prompts—will be a game-changer for productivity.

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

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 for Data Tasks

AI assistants are becoming incredibly powerful for generating SQL, drafting documentation, and even suggesting data models. Knowing how to 'talk' to these models effectively—how to craft the right prompts—will be a game-changer for productivity.

Clear & Concise Prompting
Context & Constraints
Iterative Prompt Refinement
Output Validation

Understanding Data Contracts

As data platforms grow, ensuring data quality and reliability from source systems is critical. Data contracts are formal agreements between data producers and consumers, defining schema, quality, and semantics. You'll need to understand these to ensure your pipelines are robust.

Schema Enforcement
Data Quality Expectations
Ownership & Accountability
Impact of Changes

What you’ll use

Skills this role draws on

Technical

SQL (Structured Query Language)
Dimensional Data Modelling Concepts
ELT/ETL Design Patterns (Basic)
Data Quality Principles

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
Graduate Programme / Internship
6-12 months (internship) to 1-2 years (graduate programme)
Skills to master
- Strong SQL, basic dbt, understanding of data warehousing, version control (Git), effective communication.
You're ready to move on when
- Successfully completed assigned projects with minimal supervision.
- Demonstrated consistent improvement in coding and problem-solving skills.
- Received positive feedback from mentors and peers on collaboration and initiative.
2
Junior Data Analyst / BI Developer
1-2 years
Skills to master
- Deep understanding of business data, strong SQL for reporting, basic dashboarding skills (Tableau/Looker), ability to translate business needs into data requirements.
You're ready to move on when
- Consistently delivered accurate and insightful reports/dashboards.
- Started to identify and troubleshoot data quality issues at the source.
- Expressed a strong interest in the underlying data architecture and how data is prepared.
3
Self-Taught with Portfolio
Variable (typically 1-3 years of dedicated learning)
Skills to master
- Demonstrable proficiency in SQL, Python (for data), dbt, and cloud platforms through personal projects. Strong problem-solving and debugging skills.
You're ready to move on when
- A public GitHub repository with well-documented data projects (e.g., building a data pipeline for public datasets).
- Active participation in data communities or online challenges.
- Ability to clearly articulate technical decisions and challenges faced in personal projects.

11Where this role leads

The long view:Your journey starts here, but where it goes is really up to you. We're here to provide the opportunities, the learning, and the support to help you build a truly rewarding and impactful career in data. We're excited to see what you'll achieve.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

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 Analytics Engineer I is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Data analysis and data structure design 3Level 2

Applied to your work in Analytics Engineer I

This unit aims to provide learners with an understanding of data analysis techniques and data structure design principles. Learners will be able to analyse data to identify patterns and insights, design effective data structures, and present data analysis findings clearly using appropriate visualisations and reporting methods.

How the thinking builds

Remember
Understand
Apply
Analyse
Evaluate
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.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Analytics Engineer I

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

Data Pipeline UptimeThe percentage of time your assigned data pipelines run successfully without errors.If you're responsible for 10 pipelines, and only 1 fails for an hour in a month (720 hours), that's a 99.86% uptime. We're looking for consistent reliability on your patch.Maintain a 99.5% success rate on assigned data pipelines.
Task Turnaround TimeHow quickly you complete assigned tasks, measured against the estimated effort.If a task is estimated at 3 story points and you complete it within that timeframe, that counts as a success. It's about realistic estimation and consistent delivery.Close 85% of assigned Jira tickets within the estimated story points or agreed deadline.
Data Quality IncidentsThe number of data quality bugs or errors introduced into production by your work.You push a dbt model that accidentally changes a customer ID from a number to text, breaking downstream reports. That's one incident. We expect some, especially as you learn, but we're looking for a downward trend.Introduce fewer than 5 data quality bugs per quarter.

These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Analytics Engineer I to Analytics Engineer II (Level 002), and whatever you decide comes after.

Level 2 · in progressAI Fluency→ Analytics Engineer II (Level 002)→ your design

Where this takes you

Your journey starts here, but where it goes is really up to you. We're here to provide the opportunities, the learning, and the support to help you build a truly rewarding and impactful career in data. We're excited to see what you'll achieve.

See Your Progress GrowIllustration

Analytics Engineer I

SQL (Structured Query Language)
Dimensional Data Modelling Concepts
ELT/ETL Design Patterns (Basic)
Data Quality Principles

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.

14The 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

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

Analytics Engineer II (Level 002)
2-3 years in current role
You'll move from executing tasks under guidance to independently owning and delivering specific data components or smaller projects.
- Advanced dbt Modelling: Designing and implementing more complex dbt models and data quality tests.
- Custom Data Ingestion: Building and maintaining custom data connectors for specific APIs.
- BI Data Source Optimisation: Architecting and optimising Tableau Data Sources or LookML models for performance.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of data work involves repetitive tasks, boilerplate code, and digging through documentation. But what if you could offload some of that grunt work to AI? Imagine spending less time on the tedious bits and more time on actually understanding the data and solving problems. That's exactly what AI-powered tools are starting to offer, even for junior roles.

We're not talking about replacing your job; we're talking about making it much, much easier and more productive. For an Analytics Engineer I, AI can be a brilliant co-pilot, helping you learn faster, write better code, and catch errors before they become major headaches. It's about augmenting your abilities and giving you more headspace for the interesting challenges.

SQL & dbt Co-Pilot

Use AI assistants like GitHub Copilot or Databricks Assistant to auto-generate common SQL patterns, suggest dbt model configurations, and even write initial data quality tests. This means less time typing out repetitive code and more time focusing on the logic.

Anomaly Detection Engine

Imagine tools that automatically flag when a data pipeline has gone wonky or if a key metric suddenly drops, before anyone even notices. AI-powered monitoring can help you proactively identify data quality issues or schema drift, shifting you from reactive fire-fighting to smarter, faster investigations.

Architecture Research Assistant

Got a new tool to learn? Need to compare two different data warehousing approaches? Use AI chat models to quickly summarise complex technical documentation, compare features, and even draft initial architecture ideas based on your plain-text descriptions. It's like having a super-smart research assistant at your fingertips.

Documentation & Diagram Generator

Let AI help you with the less glamorous but essential task of documentation. Tools can parse your existing SQL or dbt code to automatically generate data lineage graphs, Entity-Relationship Diagrams (ERDs), and business-friendly descriptions for your data catalog. This frees you up from manual diagramming and writing.

Common questions

How do you become an Analytics Engineer I?

Common routes in include Graduate Programme / Internship (6-12 months (internship) to 1-2 years (graduate programme)), Junior Data Analyst / BI Developer (1-2 years) and Self-Taught with Portfolio (Variable (typically 1-3 years of dedicated learning)). Times vary with prior experience.

Where can an Analytics Engineer I progress to?

This role can lead on to Analytics Engineer II (Level 002) (2-3 years in current role), depending on the skills you build.

What level is an Analytics Engineer I in the UK?

This role aligns to RQF Level 2 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for an Analytics Engineer I?

Increasingly, Prompt Engineering for Data Tasks and Understanding Data Contracts. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows an Analytics Engineer I, works on the job you actually do, and keeps going at your pace rather than a timetable's.

Searching and planning stay free. You only pay when you start learning.
Your credits are yours. Regulated, and they don't vanish when a subscription ends.
Cancel any time and billing stops. No notice period, no minimum term.

Your path, personalised

You have the map. Walking it is the part we do together.

This route runs to 10 national skill standards. That is a real journey.

Zavmo shapes a learning experience as unique as you are. It fits how you learn, your pace and the work you already do. Every step stays benchmarked to recognised national standards. That’s the plan for becoming an Analytics Engineer I: personal to you, and it still counts. The first steps are free.

Independent research finds well-designed intelligent tutoring performs nearly as well as one-to-one human tutoring: VanLehn (2011), Educational Psychologist.

A private tutor in the UK averages £35–40 an hour . Zavmo is £70/month.

A Zavmo curriculum screen: a three-month plan of nine units, ninety credits, each one an Ofqual-regulated unit the learner can tick or untick before starting. — A real plan on learn.zavmo.ai: Ofqual-regulated units, credits, and a three-month run at your own pace.

Start free No commitment. See your first steps free.

15Where to go from here

Other roles at Level 2

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

Other roles in Technical roles

Stay in the field you know and move sideways rather than up.

If you leave this industry

The skills you'll gain here—SQL, cloud data platforms, data modelling, and data governance—are highly transferable across almost any industry. Whether you want to stay in tech, move into finance, retail, or healthcare, the demand for skilled Analytics Engineers is huge. You'll be building a toolkit that opens many doors.

Not sure this is the right direction?

Work out what you actually want from work first, then come back and see which roles fit it. Takes about ten minutes.

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. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. 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.

Analytics Engineer I

What's rising in its place

Prompt Engineering for Data Tasks

Understanding Data Contracts

Skills this role draws on

Technical

How you actually get there, here

Graduate Programme / Internship

Junior Data Analyst / BI Developer

Self-Taught with Portfolio

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

Roles this leads to

No two people are taught the same way. This is one-to-one, not one-to-many.

A conversation, not a course

No two people run this the same way

This isn't a certificate you file away. It's a passport to the life you're designing.

Your journey starts here, but where it goes is really up to you. We're here to provide the opportunities, the learning, and the support to help you build a truly rewarding and impactful career in data. We're excited to see what you'll achieve.

Everything else the record holds

The career path, and where it branches