United Kingdom · Technology and data · Entry Level (0-2 years)

Analytics Engineer I

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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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 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 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 6 of 10 standards

Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 3 of 10 standards

BCS Level 4 Diploma - Digital Modular Programme for Data Analysts BCS, The Chartered Institute for IT · Level 4 · covers 2 of 10 standards

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 10 standards

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 3 of 10 standards

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 3 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 Analytics Primer NOCN Level 4 certificate for Data Analysts

Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

Data Analysis BCS Level 4 Diploma - Digital Modular Programme for Data Analysts

Data Science Foundations 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

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

Systems Fluency seeing the whole picture

Start my Analytics Engineer I plan, free

Your Plan Illustration

Built for Analytics Engineer I

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

Data Analytics Primer NOCN · covers 6 of 10 standards Level 4
Data Analytics Pearson Education Ltd · covers 4 of 10 standards Level 5
Data Analysis BCS, The Chartered Institute for IT · covers 2 of 10 standards Level 4
Data Science Foundations OTHM Qualifications · covers 3 of 10 standards Level 7
Introduction to Data Science and Big Data NCC Education Limited · covers 3 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 an Analytics Engineer I?

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 for Data Tasks
Understanding Data Contracts

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
Systems Fluencyseeing the whole picture

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

What happens when you start

Next, a free account. We build your Analytics Engineer I 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 Analytics Engineer I plan, free Ready to pay? Subscribe for £70 a month

3What this role really is

An analytics engineer builds the data pipelines and structures that make reports and dashboards possible. You lay the groundwork, making sure data flows cleanly from its source through to the dashboards business teams use. It is a foundational role in data architecture, suited to anyone keen to get into the nuts and bolts of how data systems work.

4What you'd actually use

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

Snowflake or Databricks SQL Intermediate

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 Airbyte Basic

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

Tableau or Looker Intermediate

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

Collibra or Alation Basic

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 Airflow Basic

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

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 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.

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 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.

7Would you like it

Both sides. 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.

8Who 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

9What 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.

10What 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.

11Staying 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.

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 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

13What you’ll use

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

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
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.

15Where this role leads

The long view: This role can lead to senior analytics engineering, work as an analytics architect, or managing a team of engineers.

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.

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 Analytics Primer, worked against Analytics Engineer I.

Data Analytics Primer Level 4

Applied to your work in Analytics Engineer I

This unit aims to equip learners with a foundational understanding of data analytics, including its applications and the stages of the data analysis lifecycle. Learners will explore various data types and structures, understand the role of data within an organisation, and recognise the importance of GDPR and compliance requirements in data handling.

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 Analytics Engineer I plan, free

See Your Progress Grow Illustration

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.

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

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 (2-3 years in current role), depending on the skills you build.

What new skills matter most for an Analytics Engineer I?

Increasingly, Prompt Engineering for Data Tasks and Understanding Data Contracts. 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 Analytics Engineer I 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.

Analytics Engineer I

Where should we send it?

Two short quizzes about you

Will AI change your work as an 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

One to one, on the work you already do

Everything else the record holds

The career path, and where it branches