UK

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

BI Support Analyst

As a BI Support Analyst, you transform raw engineering data into insights that drive technical decisions.

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.

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One email: what AI is changing in this job, the new skills it asks for, where it leads, 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 toLead BI Support Analyst
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Technical Data Analyst · Analytics Engineer (Technical) · Data Insights Analyst

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

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 5 of 9 standards
City & Guilds Level 3 Diploma in ICT Systems and Principles for IT Professionals City & Guilds Limited · Level 3 · covers 2 of 9 standards
City & Guilds Level 3 Certificate in ICT Systems and Principles City & Guilds Limited · Level 3 · covers 2 of 9 standards
City & Guilds Level 4 Diploma For ICT Professionals (Systems and Principles) City & Guilds Limited · Level 4 · covers 2 of 9 standards
Pearson BTEC Level 4 Higher National Certificate in Computing for England Pearson Education Ltd · Level 4 · covers 2 of 9 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 9 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Practical Data Science NOCN Level 4 certificate for Data Analysts
Business Intelligence City & Guilds Level 3 Diploma in ICT Systems and Principles for IT Professionals
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
Your Plan Illustration

Built for BI Support Analyst

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

  1. Practical Data Science NOCN · covers 5 of 9 standards Level 4
  2. Business Intelligence City & Guilds Limited · covers 2 of 9 standards Level 3
  3. Data Analytics Pearson Education Ltd · covers 4 of 9 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 a BI Support Analyst?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

How the AI economy is changing work like this

A broad read on this kind of work, not an analysis of this job on its own. Roles that share a pattern get the same answer here.

Fading: AI does more of this

AI is taking over the repetitive data extraction and initial query drafting, freeing you from routine busywork.

→
Rising: worth more because of AI

Your ability to interpret data and provide strategic insights becomes more valuable as AI handles the mundane tasks.

The new skill this role is being asked for

  • Prompt Engineering & LLM Integration
Why each one is rising

The future skills this job leans on

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

What happens when you start

Next, a free account. We build your BI Support Analyst plan as you set up, starting with the free AI Fluency module. No card.

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

Ready to pay? Subscribe for £70 a month

Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

We see you

You quietly wonder if AI will make your SQL skills less essential, but you also feel a thrill at the thought of automating the repetitive parts of your job. You hope to focus more on the strategic insights that truly engage your analytical mind.

A BI Support Analyst makes sense of engineering data, working out why build times spike or how many bugs get fixed. This work involves building dashboards and reports that help technical teams, from SRE to Platform Engineering, understand what is happening day to day. It means turning raw, often messy, data into clear, actionable insights.

4A day in the life

Not a job advert. A real day, built from what this role actually holds.

08:45
You start your day by checking the performance of your dashboards, ensuring everything is up-to-date and functioning smoothly.
10:30
An engineering manager pings you with an urgent ad-hoc data request. You dive into SQL, extracting the necessary data to answer their questions promptly.
13:15
After lunch, you collaborate with a junior analyst, reviewing their SQL query and providing feedback to help them improve.
15:45
You spend the afternoon investigating a data quality issue, working with senior analysts to propose a robust solution.

5What you'd actually use

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

Tableau Desktop / Looker Intermediate

Building and maintaining interactive dashboards for engineering teams, creating calculated fields, and setting up filters and parameters.

SQL (DBeaver / DataGrip) Intermediate

Writing standard queries (JOINs, GROUP BY, WHERE, basic window functions) to extract and transform data from our data warehouse (Snowflake/BigQuery) for analysis and dashboard feeds.

Python (pandas) Basic

Reading and making minor modifications to existing scripts for simple data cleaning, transformation, or API calls. You won't be building complex models from scratch, but you should be comfortable with the basics.

Jira / GitHub / Jenkins / Datadog User

Pulling data and understanding the basic schema of these systems via pre-built connectors or simple API calls. You'll need to know what kind of data lives where and how it relates to engineering processes.

Confluence / Jira / Slack / MS Teams User

Documenting your work, managing tasks, communicating findings, and collaborating with your team and stakeholders. These are your everyday communication and project management tools.

6What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Dashboard Design & Visualisation Follows templates, all designs reviewed by senior. Independently designs dashboards for specific teams, consults Lead for complex visualisations or new patterns. Defines new dashboard design standards, approves designs for junior analysts.
SQL Query Optimisation Writes basic queries, relies on senior for optimisation. Optimises routine queries, seeks input on complex performance issues. Architects complex, performant queries; reviews and refactors others' SQL.
Data Source Integration Uses pre-built connectors, needs guidance for new sources. Integrates new data sources using existing connectors/APIs with some guidance; flags new API needs. Designs and implements new data ingestion pipelines, evaluates new integration tools.
Prioritisation of Ad-hoc Requests Manager assigns and prioritises all requests. Prioritises own queue of routine requests, consults Lead on conflicting or high-impact requests. Manages team's ad-hoc request backlog, negotiates with stakeholders on delivery timelines.

7How 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.

Dashboard Accuracy & Freshness
The reliability of the data presented in your dashboards and reports. Are the numbers correct? Is the data up-to-date?
Target · >98% data accuracy; <1 hour data latency for critical dashboards

If a dashboard shows 100 deployments, but the underlying source system only recorded 95, that's an accuracy issue. If a dashboard hasn't refreshed in 3 hours, that's a freshness issue. We're aiming for near-perfect.

Ad-hoc Request Resolution Time
How quickly you turn around specific, one-off data requests from engineering teams.
Target · 80% of requests resolved within 48 business hours

An Engineering Manager asks for a list of all PRs merged by Team X last quarter. You get it to them within a day, allowing them to prepare for their planning meeting on time.

Self-Service Dashboard Adoption
The extent to which your dashboards are actually used by the target audience without needing your direct intervention.
Target · Minimum 50 unique users per dashboard per month

Your 'Team Velocity' dashboard is viewed by 65 unique users across 10 engineering teams in a month, showing it's a valuable tool they're using independently.

Data Quality Improvement Contributions
Your proactive identification and contribution to fixing underlying data quality issues.
Target · Identify and document 2-3 significant data quality issues per quarter; contribute to resolution of at least 1 per quarter.

You notice that 'component' tags in Jira are inconsistent across teams. You flag this, document the impact, and work with a Lead BI Analyst to propose a standardisation plan.

Stakeholder Satisfaction & Trust
How happy your stakeholders are with your work, and whether they see you as a reliable source of truth for technical data.
  • Positive feedback in 1:1s or team meetings
  • stakeholders proactively coming to you for advice
  • their willingness to trust your data without extensive questioning
  • positive comments in quarterly feedback rounds.
Documentation Clarity & Maintainability
The quality and comprehensiveness of your documentation for dashboards, data models, and queries.
  • Other analysts (including new starters) can easily understand and update your work
  • fewer questions about how a dashboard works or where data comes from
  • positive feedback during code reviews or knowledge transfer sessions.
Proactive Problem Identification
Your ability to spot potential data issues or opportunities for new insights before being asked.
  • You flag an unexpected dip in deployment frequency before the Engineering Director notices
  • you suggest a new dashboard idea based on observing a recurring question
  • you identify an inconsistency in source system data and bring it to the team's attention.
Informal Mentorship & Team Contribution
How well you help newer team members get up to speed or contribute to overall team knowledge.
  • New starters come to you with questions
  • you share useful tips or resources in team meetings
  • you contribute to internal knowledge base articles
  • positive feedback from junior colleagues or your manager about your helpfulness.

8Would you like it

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

What people enjoy
Making a Tangible Impact

You'll feel a real buzz when an Engineering Manager tells you your dashboard helped them reallocate resources, or a Product Owner uses your data to prioritise a critical feature. Your work directly informs decisions.

Seeing your 'Deployment Frequency' dashboard lead to a team optimising their release process, resulting in more frequent, smaller deployments.

Solving Complex Puzzles

The challenge of taking disparate, messy data and transforming it into a clear, coherent story is what gets you up in the morning. You love the 'aha!' moment when you finally crack a tricky data problem.

Successfully combining data from Jira, GitHub, and Jenkins to show the true end-to-end cycle time for a feature, despite inconsistent tagging across systems.

Continuous Learning & Growth

You'll constantly be learning new tools, new data sources, and new ways to analyse technical performance. The technical landscape changes fast, and you'll be at the forefront of understanding its impact through data.

Picking up a new SQL window function or a Python library to solve a data problem you couldn't tackle before, and then sharing that knowledge with the team.

What frustrates people
  • Garbage In, Garbage Out: Spending 60% of your time cleaning data because source systems are inconsistent.
  • The '5-Minute' Urgent Request: An Engineering Director needs a 'quick number' that actually takes half a day to pull and validate.
  • Weaponised Metrics: Seeing your carefully crafted dashboards used to unfairly judge teams, rather than for coaching and improvement.
  • Endless Definition Debates: Getting stuck in a 'Product vs. Engineering' war over the exact definition of a 'bug' or 'story point'.
  • Shadow BI: Discovering a Senior Engineer has built their own conflicting reporting in a Google Sheet that you now have to reconcile.
  • The Moving Goalposts: A stakeholder approves a dashboard design, then asks for a complete structural change the day before launch.
  • Explaining Causation vs. Correlation: Repeatedly clarifying that a drop in PR size doesn't necessarily cause an increase in bugs.
What this role does not give you
  • A perfectly clean, well-structured data environment—you'll be part of making it better, but it won't be handed to you.
  • Complete autonomy over strategic direction—you'll contribute, but the Lead or Manager will set the broader roadmap.
  • A purely technical coding role—you'll code, but also spend time on data interpretation, stakeholder communication, and documentation.
  • A quiet, uninterrupted work environment—expect ad-hoc questions and shifting priorities.

9Who you work with

This role directly impacts the efficiency and effectiveness of our engineering organisation. By providing clear, accurate, and timely data, you'll help teams identify bottlenecks, track progress against goals (like DORA metrics), and ultimately improve our development processes. Your work helps ensure that engineering leaders have the right information to make strategic decisions about resource allocation, technical debt, and process improvements. Honestly, without good BI, we're just guessing.

Inside the business
  • Engineering Managers (SRE, Platform, Feature Teams)
  • Product Owners
  • Senior Engineers
  • Lead BI Support Analyst
  • Technical Programme Managers
Outside the business
  • N/A (primarily internal-facing)

10What you need before you start

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

  • Proven ability to write complex SQL queries for data extraction and transformation (e.g., using CTEs, window functions, various JOIN types).
  • Demonstrable experience building and maintaining interactive dashboards in a major BI tool like Tableau, Looker, or Power BI.
  • Experience working with raw data from technical systems (e.g., Jira, GitHub, logging platforms) and understanding their schemas.
  • A track record of translating business questions into analytical requirements and delivering actionable insights.
  • Strong problem-solving skills, with a focus on data validation and ensuring accuracy.

11What to practise next

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

Advanced Data Modelling & dbt

As our data landscape grows, we need more robust, version-controlled, and well-documented data models. dbt is becoming the industry standard for this, allowing us to build reliable data pipelines.

Modular Data Transformations · Data Quality Testing with dbt · Macros & Packages · Version Control for Data Models

  • This month: Complete an online course on dbt fundamentals and build a small project with it.
  • Month 2: Contribute to an existing dbt project at work, starting with documentation or adding simple tests.
  • Month 3: Propose a new dbt model for a specific data source or reporting need.
  • Month 4: Get certified in dbt (if available and relevant).

Quick win: Familiarise yourself with our existing dbt codebase (if we have one) and understand its structure. If not, start exploring dbt tutorials online.

Advanced Python for Data Engineering

Beyond basic pandas, you'll need to use Python for more robust data extraction (APIs), transformation, and even light automation. It's about building more flexible and scalable data solutions.

API Integration (requests library) · Error Handling & Logging · Data Structures & Algorithms · Virtual Environments & Package Management

  • This month: Complete an intermediate Python course focusing on data manipulation and API calls.
  • Month 2: Build a small Python script to automate a repetitive data extraction task you currently do manually.
  • Month 3: Refactor an existing Python script to improve its error handling and logging capabilities.
  • Month 4: Explore a data orchestration tool like Airflow (even if just locally) to understand how Python scripts are scheduled.

Quick win: Start using Python for any data cleaning tasks that are too complex for SQL. Automate a simple file conversion or data aggregation.

12Staying current once you are in

What people here do to keep up
  • Regularly contributing to open-source projects related to data analytics or engineering, especially those involving Python or SQL.
  • Attending industry conferences or meetups (e.g., Data London, Tableau User Groups) to stay current with trends and network.
  • Completing online courses on advanced SQL, Python for data engineering, or specific BI tool features (e.g., LookML, Tableau LODs).
  • Participating in internal knowledge-sharing sessions or presenting on a new technique you've learned to the team.
  • Reading relevant blogs, books, or research papers on technical data analytics and DevOps metrics.

13The 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

Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours. Analysts who figure this out will outproduce their peers significantly. It's about working smarter, not just harder.

  • Context Windows & Token Limits
  • Temperature Settings for Tasks
  • RAG Architectures
  • Output Validation & Hallucination Detection
  • Prompt Chaining

14What you’ll use

What you’ll use

Skills this role draws on

Technical

  • DORA Metrics Analysis
  • Agile/Scrum Metrics
  • Data Modeling (Kimball Methodology)
  • Stakeholder Requirements Elicitation
  • Data Lineage and Governance
  • Root Cause Analysis (RCA)

15The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Junior BI Analyst / Associate Data Analyst

    1-2 years

    Skills to master

    • Core SQL querying, basic dashboard building, data cleaning fundamentals, understanding of business requirements.

    You're ready to move on when

    • Can independently build simple dashboards from existing data models.
    • Consistently delivers accurate data pulls for routine requests.
    • Proactively identifies and flags basic data quality issues.
    • Demonstrates a solid understanding of the data lifecycle.
  2. 2

    Technical Support Analyst (with data focus)

    2-3 years

    Skills to master

    • Troubleshooting technical issues, understanding system logs and metrics, basic scripting (e.g., Python), customer communication.

    You're ready to move on when

    • Can extract and analyse log data to identify patterns or root causes.
    • Understands the architecture of various technical systems (e.g., ticketing, monitoring).
    • Has used scripting to automate data collection or analysis tasks.
    • Effectively communicates technical findings to non-technical audiences.
  3. 3

    Data Intern / Graduate Programme

    1-2 years

    Skills to master

    • Foundational data analysis techniques, exposure to BI tools, understanding of data governance principles, project-based learning.

    You're ready to move on when

    • Successfully completed multiple data-related projects.
    • Demonstrates strong analytical aptitude and eagerness to learn.
    • Has a portfolio of work showcasing SQL and visualisation skills.
    • Receives positive feedback on collaboration and initiative.

16How people get here · where they go next

Came from
Junior BI Analyst / Associate Data Analyst
1-2 years
You mastered the basics of SQL querying and dashboard building, setting a solid foundation for more complex data analysis.
You are here
BI Support Analyst
Mid-Level (2-5 years)
A BI Support Analyst makes sense of engineering data, working out why build times spike or how many bugs get fixed. This work involves building dashboards and reports that help technical teams, from SRE to Platform Engineering, understand what is happening day to day. It means turning raw, often messy, data into clear, actionable insights.
Goes to
Senior BI Support Analyst (Level 003)
3-5 years in current role
You will lead workstreams, mentor junior analysts, and tackle more complex, non-routine problems, expanding your influence within the organisation.

The long view: This work can lead to leading a team of analysts or specialising further as a principal analytics engineer, focused on complex data modelling and technical strategy.

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 BI Support Analyst is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • Senior BI Support Analyst (Level 003)

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 Practical Data Science, worked against BI Support Analyst.

Practical Data Science Level 4

Applied to your work in BI Support Analyst

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.

The Explorer Last time we explored using a new visualisation type in your dashboards. How did it go?

You It was challenging at first, but I think it made the data more understandable.

The Explorer Great! Let's build on that by trying a different approach to summarise complex data—perhaps a heat map for your next project.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

A year from now

A year from now, you confidently lead projects that transform data into strategic insights, becoming a trusted advisor to your engineering teams.

See Your Progress Grow Illustration
BI Support Analyst
  • DORA Metrics Analysis
  • Agile/Scrum Metrics
  • Data Modeling (Kimball Methodology)
  • Stakeholder Requirements Elicitation
  • Data Lineage and Governance
  • Root Cause Analysis (RCA)
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

BI Support Analyst is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior BI Support Analyst (Level 003)

    3-5 years in current role

    You'll move from independently executing tasks to leading entire workstreams and mentoring others. Your scope will broaden, and you'll tackle more ambiguous, non-routine problems.

    • Advanced Data Modelling Designing complex data models for new domains.
    • Data Governance Leadership Driving initiatives to improve data quality and consistency across the organisation.
    • Advanced Scripting Building more robust ETL pipelines using Python and dbt.
    • Tool Evaluation Researching and recommending new BI or data tools.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a big chunk of a BI Support Analyst's job involves repetitive tasks, digging through logs, and trying to make sense of messy data. What if you could get back hours every week to focus on the really interesting stuff – the deep analysis, the strategic insights, the actual problem-solving? That's where AI comes in. We're not just talking about buzzwords; we're talking about practical tools that will genuinely make your daily work easier and faster.

Imagine having a super-smart assistant that helps you write SQL, spot anomalies, and even draft your dashboard summaries. That's the future of Business Intelligence Support, and it's happening now. We're embracing AI to take the grunt work out of your hands, allowing you to deliver more impact and less toil. Here’s a sneak peek at how AI tools will become your best mate in this role:

Natural Language to SQL Generation

Use AI copilots to translate plain English requests—like 'Show me the top 5 repos with the highest code churn last month'—directly into functional SQL queries. This drastically cuts down the time you spend on routine ad-hoc requests, letting you focus on validating the output rather than writing every line from scratch.

Anomaly Detection & Alerting

Configure AI models to constantly monitor key technical metrics, such as build failures, deployment frequency, or API error rates. These tools will automatically flag statistically significant anomalies, meaning you can investigate issues and even alert stakeholders before they even notice something's off. It's about being proactive, not reactive.

Automated Documentation & Data Discovery

Leverage AI tools to scan our database schemas, query logs, and even dashboard definitions. This helps automatically generate data dictionaries, document data lineage, and provide context for data fields. It makes it much faster for you, and anyone else, to find and understand the data you need without endless searching.

Dashboard Narrative Generation

Once your dashboard is built and the data is clear, use generative AI to create a first draft of the 'Key Takeaways' or 'Executive Summary' section. This converts the quantitative data into a qualitative story, making it much easier for busy leaders to quickly grasp the core insights without having to dig through every chart.

Common questions

Common questions

How do you become a BI Support Analyst?

Common routes in include Junior BI Analyst / Associate Data Analyst (1-2 years), Technical Support Analyst (with data focus) (2-3 years) and Data Intern / Graduate Programme (1-2 years). Times vary with prior experience.

Where can a BI Support Analyst progress to?

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

What new skills matter most for a BI Support Analyst?

Increasingly, Prompt Engineering & LLM Integration. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

18Where to go from here

Other roles at Level 3

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

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built here, SQL, data visualisation, understanding technical systems, and translating data into insights, transfer well elsewhere. They can lead to Data Analyst roles in other departments such as Product, Marketing or Finance, or towards Data Engineering or Data Science with further programming skills. The technical knowledge involved is useful across many industries.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free AI Fluency module 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.