UK

United Kingdom · Technical roles · Mid-Level (2-5 years)

Analytics Engineer II

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

Also advertised as Mid-Level Data Engineer · Data Pipeline Developer · BI Data Specialist · Data Modeller

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 II

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

You'll be building and looking after the data pipelines and models that power our business decisions. This isn't just about writing code; it's about making sure the right data gets to the right people, reliably and accurately. Think of yourself as a crucial part of the data plumbing system, making sure everything flows smoothly from source to dashboard.

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 complex SQL queries for data extraction, transformation, and loading, as well as optimising query performance.

dbt CoreIntermediate

Building and maintaining data models, defining transformations, and implementing data quality tests following existing patterns and best practices.

Fivetran or AirbyteBasic

Monitoring and troubleshooting pre-built data connectors; configuring new, simple data sources for ingestion into the data warehouse.

Tableau or LookerIntermediate

Building and maintaining dashboards and reports from pre-defined data sources, understanding basic UX principles for data visualisation.

Collibra or AlationBasic

Using the data catalogue to find and understand existing data assets, and annotating data definitions based on input from subject matter experts.

Apache Airflow (or similar orchestrator)Basic

Monitoring and triggering existing data pipelines (DAGs), and understanding basic concepts of task dependencies and scheduling.

Python (pandas, NumPy)Intermediate

Writing custom scripts for data cleaning, transformation, and API integrations where standard connectors aren't sufficient.

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 choiceComing inWhere you are nowThe step above
Data Model Design for a New FeatureProposes initial design, requires full review and approval from Senior Engineer.Designs and implements the model independently for a well-defined project; consults Senior Engineer on complex trade-offs or new patterns. Presents to relevant stakeholders for feedback.Leads the design of complex, multi-domain data models, sets design patterns, and mentors junior engineers through the process. Approves final designs.
Troubleshooting a Pipeline FailureIdentifies the error, gathers initial context, and escalates to Senior Engineer for resolution.Diagnoses the root cause, implements a fix (after manager approval for critical production systems), and documents the resolution. Communicates impact to relevant stakeholders.Leads complex incident response, coordinates across teams (e.g., source system owners), and designs preventative measures. Mentors others on debugging techniques.
Tool/Technology Selection for a Project ComponentResearches options as directed by Senior Engineer, provides summaries.Proposes specific tools or technologies for a project component (e.g., a specific Python library for data cleaning), provides a clear rationale, and seeks approval from Senior Engineer.Evaluates and recommends major tools or technologies for a workstream, considering cost, scalability, and maintainability. Leads proof-of-concept efforts.
Prioritisation of Your Own WorkloadFollows explicit instructions from manager; flags any conflicts immediately.Prioritises daily tasks based on sprint goals and agreed deadlines; flags any potential blockers or conflicts to manager during weekly check-ins.Manages workload across multiple projects, proactively reprioritises based on business impact, and helps the team manage dependencies.

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 manual intervention or failure.
Target · Maintain 99.5% success rate on assigned data pipelines.

If you own 10 pipelines, and one fails for 4 hours in a week (168 hours total), your uptime for that pipeline is (168-4)/168 = 97.6%. We're looking for consistent high performance across all your owned components.

Task Turnaround & Delivery
How quickly and efficiently you complete assigned data engineering tasks and project components.
Target · Close 85% of assigned Jira tickets within the estimated story points or agreed deadline.

You're assigned a ticket estimated at 8 story points. If you complete it within the sprint, that's a win. If you consistently miss estimates, we'll need to figure out why—maybe the estimates were off, or there were blockers.

Data Quality Incidents
The number of data quality issues (e.g., missing data, incorrect values, schema mismatches) introduced by your work that impact downstream users.
Target · Introduce fewer than 5 data quality bugs per quarter that require a hotfix or significant rework.

A product analyst flags that the 'user_signup_date' in the marketing dashboard is sometimes null, tracing it back to a dbt model you built. That counts as an incident. We expect some, especially when dealing with messy source data, but we want you to catch most of them before they hit production.

Query Performance Optimisation
The improvement in execution time for key queries or dashboards that rely on your data models.
Target · Improve performance of at least 2 critical queries/dashboards by an average of 15% each quarter.

The 'Daily Sales Report' dashboard takes 30 seconds to load. You re-index a table or optimise a dbt model, and it now loads in 20 seconds. That's a 33% improvement, which is brilliant.

Reliability & Proactiveness
How consistently you deliver high-quality work and anticipate potential issues before they become problems.
  • You're the person who flags a potential schema change in a source system before it breaks your pipeline. You consistently test your code thoroughly. Your manager rarely has to chase you for updates
  • you're usually ahead of it. Colleagues trust your work implicitly.
Collaboration & Communication
How effectively you work with other team members and business stakeholders, explaining technical concepts clearly.
  • You actively participate in sprint planning and stand-ups. You clearly explain why a data model needs to be structured a certain way to a product analyst. You ask clarifying questions when requirements are vague, rather than just building something that might be wrong. You're good at giving and receiving feedback during code reviews.
Documentation & Maintainability
The quality and completeness of the documentation for the data assets and pipelines you build or maintain.
  • Your dbt models have clear descriptions and column-level comments. You update the data catalogue when you make changes. Another engineer can pick up your code and understand what it does without needing to ask you a dozen questions. You leave the codebase in a better state than you found it.
Problem-Solving & Adaptability
Your ability to diagnose and fix issues, and adjust to changing priorities or technical challenges.
  • When a pipeline breaks, you systematically debug it, rather than just restarting it. You can propose alternative solutions when a requirement is technically difficult. You don't get flustered when an 'urgent' request comes in and can reprioritise your work effectively (after checking with your manager, of course).

5Would you like it

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

What people enjoy
Solving Complex Puzzles

You get a real buzz from figuring out why a data pipeline failed, or how to transform a really messy dataset into something clean and usable. It's like being a detective, following clues through logs and code.

Spending an afternoon debugging a tricky SQL query that's returning incorrect results, finally finding the subtle join condition error, and seeing the correct numbers appear.

Building Reliable Systems

You feel a sense of pride when your data models are consistently accurate and your pipelines run without a hitch. You like knowing your work is the foundation for important business decisions.

Seeing a new dashboard go live, knowing that the underlying data model you built is solid and will provide trustworthy insights for months to come.

Learning & Growing Technically

You're always keen to pick up new tools, learn a new cloud service, or deepen your understanding of data architecture patterns. You enjoy the challenge of mastering new technical skills.

Taking the initiative to learn a new dbt feature or a different way to handle slowly changing dimensions, and then applying it to improve an existing model.

What frustrates people
  • Fighting the daily battle against 'Shadow IT'—where business teams spin up their own ungoverned databases and dashboards, creating multiple sources of truth that you then have to reconcile.
  • Explaining to non-technical stakeholders why you can't just 'add one more column' to a report when it requires a fundamental change to the underlying data model that will take a few days, not hours.
  • The source system team that swears 'nothing changed,' yet your pipeline has been failing for 6 hours because they subtly changed a data type from an integer to a string.
  • Spending a significant chunk of your time on 'data plumbing' and cleaning messy, inconsistent source data, and less time on building exciting new architecture.
  • The constant pressure to deliver short-term fixes and dashboards, which can sometimes conflict with the need to invest in long-term, scalable architecture and pay down tech debt.
  • Being the ultimate owner of the data's quality, even when the root cause of errors lies in upstream operational systems you have no control over.
What this role does not give you
  • A perfectly clean, well-documented data environment from day one. You'll be part of making it better, not inheriting perfection.
  • Total autonomy on strategic direction. You'll own components, but the overall architectural vision comes from more senior engineers.
  • A quiet, heads-down coding environment all the time. You'll need to talk to people, understand their needs, and explain your solutions.

6Who you work with

This role directly underpins the reliability and accuracy of our entire analytics ecosystem. Your work ensures that business teams have access to the right data at the right time, preventing costly errors and enabling data-driven growth. Frankly, if you get it wrong, the whole company feels it.

Inside the business
  • Product Analysts (who use your data for feature analysis)
  • Marketing Operations (who need campaign performance data)
  • Sales Operations (for CRM data and sales performance reporting)
  • Data Scientists (who might use your cleaned data for advanced models)
  • Your immediate Analytics Engineering team
Outside the business
  • Data platform vendors (e.g., Snowflake support, dbt Labs community)

7What 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 in a data engineering, analytics engineering, or similar role, building and maintaining data pipelines.
  • Proven ability to write complex SQL queries and optimise them for performance.
  • Experience with a modern data transformation tool like dbt (Data Build Tool).
  • Familiarity with at least one cloud data warehouse (Snowflake, Google BigQuery, or Amazon Redshift).
  • Experience building dashboards and reports in a major BI tool (e.g., Tableau, Looker, Power BI).
  • A solid understanding of data modelling concepts (e.g., star schema, normalisation).
  • Basic scripting skills in Python for data manipulation or automation.

8What to practise next

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

Advanced dbt Development

Moving beyond basic models to designing complex, multi-layered dbt projects (e.g., staging, intermediate, marts) and developing custom macros for reusability. This is about building scalable, maintainable data transformation pipelines.

Modular dbt Projects · Custom Macros · CI/CD for dbt · Performance Tuning

  • This month: Explore the dbt documentation for advanced features like custom materialisations and hooks.
  • Month 2: Contribute a new custom macro to our dbt project or refactor an existing one for better reusability.
  • Month 3: Take a dbt certification or complete an advanced dbt course online.
  • Month 4: Lead a small project to implement CI/CD best practices for a dbt sub-project.

Quick win: Look for repetitive SQL patterns in our current dbt models and think about how a custom macro could simplify them.

Cloud Data Platform Specialisation

As we lean more heavily into cloud services, you'll need to go beyond basic understanding. This means getting really good at how our specific cloud platform (e.g., AWS or GCP) works, including managed services for data processing and orchestration.

Serverless Compute (e.g., AWS Lambda, GCP Cloud Functions) · Managed Data Services (e.g., AWS Glue, GCP Dataflow) · IAM Roles & Security · Cost Optimisation

  • This month: Pick one cloud data service (e.g., AWS Glue, GCP Dataflow) and complete an online tutorial or course.
  • Month 2: Propose and build a small proof-of-concept using that service to solve a current data problem.
  • Month 3: Work towards a cloud certification (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).
  • Month 4: Actively participate in discussions about cloud architecture and cost optimisation within the team.

Quick win: Familiarise yourself with the billing dashboard for our cloud provider. Where are our data costs coming from?

9Staying current once you are in

What people here do to keep up
  • Actively participate in online data communities (e.g., dbt Slack, Data Engineering Weekly newsletter).
  • Attend relevant industry webinars or virtual conferences (e.g., Coalesce, Data + AI Summit).
  • Contribute to open-source data projects if you're passionate about a particular tool.
  • Take online courses on advanced SQL, Python for data, or cloud data services (e.g., DataCamp, Coursera, Udemy).
  • Read books and blogs on data architecture, data governance, and data modelling best practices.

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 & LLM Integration

Honestly, competitors are already using AI to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly. Your value will shift to validating, interpreting, and knowing when NOT to trust the output.

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

Your PlanIllustration

Built for Analytics Engineer II

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

  1. Practical Data ScienceNOCN · covers 6 of 9 standardsLevel 4
  2. Data AnalyticsPearson Education Ltd · covers 5 of 9 standardsLevel 4
  3. Data AnalysisHighfield Qualifications · covers 2 of 9 standardsLevel 3
  4. Data analysis and designPearson Education Ltd · covers 4 of 9 standardsLevel 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.

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 AI to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly. Your value will shift to validating, interpreting, and knowing when NOT to trust the output.

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

Data Contracts & Data Mesh Principles

As our data ecosystem grows, 'schema drift' and broken pipelines are becoming a nightmare. Data contracts are the solution, formalising agreements between data producers and consumers. Understanding Data Mesh means thinking about data as a product, owned by domain teams, which changes how we architect everything.

  • Contract-First Design
  • Schema Evolution
  • Data Product Thinking
  • Domain Ownership

What you’ll use

Skills this role draws on

Technical

  • Dimensional Data Modelling
  • ELT/ETL Design Patterns
  • Agile Data Development
  • Data Quality & Testing
  • SQL Optimisation

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

    Analytics Engineer I / Junior Data Engineer

    1-2 years

    Skills to master

    • Mastering SQL, understanding basic data modelling, dbt fundamentals, and troubleshooting simple pipeline issues. Getting comfortable with our core tech stack.

    You're ready to move on when

    • Consistently delivers accurate and well-tested dbt models.
    • Can independently resolve common data quality issues.
    • Proactively identifies opportunities for minor data pipeline improvements.
    • Demonstrates a solid understanding of our data warehouse structure.
  2. 2

    Data Analyst (with strong technical bent)

    2-3 years

    Skills to master

    • Moving beyond just analysing data to understanding how it's built. Learning data pipeline tools, dbt, and basic cloud concepts. Shifting from consumption to creation of data assets.

    You're ready to move on when

    • Regularly writes complex SQL queries for data extraction and manipulation.
    • Has built small, personal data projects or automation scripts.
    • Shows a keen interest in data infrastructure and how data is prepared.
    • Can identify inefficiencies in existing data preparation processes.
  3. 3

    Software Developer (with data interest)

    2-3 years

    Skills to master

    • Applying software engineering principles (testing, CI/CD, version control) to data problems. Learning data warehousing concepts, dimensional modelling, and specific data tools like dbt and Snowflake.

    You're ready to move on when

    • Strong programming skills (e.g., Python, Java) and software development best practices.
    • Experience with database systems and SQL.
    • Demonstrates an interest in data systems and analytics.
    • Can quickly pick up new data-specific tools and frameworks.

11Where this role leads

The long view:Your journey in analytics engineering is a continuous one of learning and impact. We're committed to providing the opportunities and support for you to build a truly rewarding career here, whether you choose to deepen your technical expertise or move into leadership.

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 II 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.

Where it leads

Roles this leads to

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:

Practical Data ScienceLevel 4

Applied to your work in Analytics Engineer II

The objective of this unit is to enable learners to apply statistical and machine learning techniques to solve data science problems. Learners will gain practical skills in regression analysis, forecasting, model creation and tuning, natural language processing, and data mining to extract valuable insights from data.

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.

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.

  1. Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
  2. 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.
  3. 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.
  4. 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.
  5. 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 II

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 manual intervention or failure.If you own 10 pipelines, and one fails for 4 hours in a week (168 hours total), your uptime for that pipeline is (168-4)/168 = 97.6%. We're looking for consistent high performance across all your owned components.Maintain 99.5% success rate on assigned data pipelines.
  • Task Turnaround & DeliveryHow quickly and efficiently you complete assigned data engineering tasks and project components.You're assigned a ticket estimated at 8 story points. If you complete it within the sprint, that's a win. If you consistently miss estimates, we'll need to figure out why—maybe the estimates were off, or there were blockers.Close 85% of assigned Jira tickets within the estimated story points or agreed deadline.
  • Data Quality IncidentsThe number of data quality issues (e.g., missing data, incorrect values, schema mismatches) introduced by your work that impact downstream users.A product analyst flags that the 'user_signup_date' in the marketing dashboard is sometimes null, tracing it back to a dbt model you built. That counts as an incident. We expect some, especially when dealing with messy source data, but we want you to catch most of them before they hit production.Introduce fewer than 5 data quality bugs per quarter that require a hotfix or significant rework.
  • Query Performance OptimisationThe improvement in execution time for key queries or dashboards that rely on your data models.The 'Daily Sales Report' dashboard takes 30 seconds to load. You re-index a table or optimise a dbt model, and it now loads in 20 seconds. That's a 33% improvement, which is brilliant.Improve performance of at least 2 critical queries/dashboards by an average of 15% each 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 II to Senior Analytics Engineer (Level 3), and whatever you decide comes after.

Level 3 · in progressAI Fluency→ Senior Analytics Engineer (Level 3)→ your design
Where this takes you

Your journey in analytics engineering is a continuous one of learning and impact. We're committed to providing the opportunities and support for you to build a truly rewarding career here, whether you choose to deepen your technical expertise or move into leadership.

See Your Progress GrowIllustration
Analytics Engineer II
  • Dimensional Data Modelling
  • ELT/ETL Design Patterns
  • Agile Data Development
  • Data Quality & Testing
  • SQL Optimisation
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 II is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior Analytics Engineer (Level 3)

    3-5 years in current role

    You'll move from owning components to leading entire workstreams and small projects, designing data models for specific business domains, and mentoring junior engineers.

    • Domain Data Modelling: Designing comprehensive data models for specific business areas (e.g., Marketing, Finance).
    • Advanced Cloud Data Services: Deeper expertise in specific cloud services for data processing and orchestration.
    • Data Governance Implementation: Actively contributing to and enforcing data governance policies within your domain.
    • Performance Architecture: Optimising entire data pipelines and models for cost and performance at scale.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, some parts of data engineering can feel a bit like Groundhog Day. But what if you could automate the tedious bits and focus on the really interesting, challenging problems? AI isn't here to replace you; it's here to give you superpowers. Imagine getting back 15-25 hours every week—that's nearly two full days—to spend on deeper analysis, learning new skills, or just enjoying a longer weekend.

For an Analytics Engineer II, AI tools can dramatically cut down on repetitive coding, documentation, and even help you spot issues before they become major headaches. You'll be able to deliver more, faster, and with higher quality, making you an even more valuable member of the team. Here's a peek at how you'll be using AI every day:

SQL & dbt Co-Pilot

Use AI assistants like GitHub Copilot or Databricks Assistant to auto-generate boilerplate SQL queries, write dbt model configurations, and even automatically create documentation (those YAML files) directly from your existing code. It's like having an extra pair of hands that knows all the syntax.

Anomaly Detection Engine

Implement AI-powered monitoring tools (think Monte Carlo or Anomalo) that automatically detect data quality issues, unexpected schema changes ('schema drift'), and pipeline failures. This means you're shifting from reactive fire-fighting—only finding out about problems when a dashboard breaks—to proactive investigation, catching issues before anyone else notices.

Architecture Research Assistant

Use AI chat models (like ChatGPT or Claude) to quickly summarise technical documentation for new tools or features, compare the pros and cons of competing platforms (e.g., 'Compare Snowflake and Databricks on data governance features'), and even draft initial architecture diagrams based on your plain-text descriptions. It's like having a hyper-efficient research intern.

Documentation & Diagram Generator

Leverage AI tools that can parse your codebase or connect to your data warehouse to automatically generate data lineage graphs, ERDs (Entity-Relationship Diagrams), and business-friendly documentation for our data catalogue. This means less time on tedious manual updates and more time building.

Common questions

Common questions

How do you become an Analytics Engineer II?

Common routes in include Analytics Engineer I / Junior Data Engineer (1-2 years), Data Analyst (with strong technical bent) (2-3 years) and Software Developer (with data interest) (2-3 years). Times vary with prior experience.

Where can an Analytics Engineer II progress to?

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

What level is an Analytics Engineer II in the UK?

This role aligns to RQF Level 3 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 II?

Increasingly, Prompt Engineering & LLM Integration and Data Contracts & Data Mesh Principles. 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 II, 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 9 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 II: 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 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 3

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 build here are highly transferable across almost any industry. Every modern business needs robust data infrastructure. You could move into FinTech, E-commerce, Healthcare, or even specialised data consultancies. The demand for skilled analytics engineers is only growing.

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.

This role profile is © 2026Growth 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. 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.