United Kingdom · Technology and data · Lead (8-12 years)

Lead Business Intelligence Developer

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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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 bandLead (8-12 years)
Direct reports3-8 reports
Reports toBI Manager or Director of Business Intelligence
UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Staff BI Engineer · Principal BI Developer · BI Architect · Senior Data Warehouse Developer

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

Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 11 standards

Qualifi Level 5 Diploma in AI Qualifi Ltd · Level 5 · covers 3 of 11 standards

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 3 of 11 standards

Pearson BTEC Level 5 Higher National Diploma in Digital Technologies Pearson Education Ltd · Level 5 · covers 3 of 11 standards

Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 2 of 11 standards

Pearson BTEC Level 5 Higher National Diploma in Business Pearson Education Ltd · Level 5 · covers 1 of 11 standards

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

The units you would start with

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

Visualisation Qualifi Level 5 Diploma in AI

Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science

Business Intelligence Pearson BTEC Level 5 Higher National Diploma in Cloud Computing

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 Lead Business Intelligence Developer plan, free

Your Plan Illustration

Built for Lead Business Intelligence Developer

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

Data Analytics Pearson Education Ltd · covers 4 of 11 standards Level 5
Visualisation Qualifi Ltd · covers 3 of 11 standards Level 5
Introduction to Data Science and Big Data NCC Education Limited · covers 3 of 11 standards Level 5
Business Intelligence Pearson Education Ltd · covers 2 of 11 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 Lead Business Intelligence Developer?

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

The new skills this role is being asked for

Prompt Engineering & LLM Integration for Data Tasks
Data Mesh & Data Fabric Concepts

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 Lead Business Intelligence Developer 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.

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3What this role really is

A Lead Business Intelligence Developer designs data models, sets the technical direction for a reporting platform, and makes sure it works day to day. The work covers architecture, mentoring and influencing decisions, alongside building dashboards.

4What you'd actually use

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

Power BI / Tableau Expert

Designing and optimising complex, interactive dashboards with advanced features (e.g., complex DAX in Power BI, LOD calculations in Tableau). Leading performance tuning and best practices for dashboard development. Mentoring others on advanced features and data visualisation principles. Governing content strategy.

SQL (PostgreSQL/T-SQL/Snowflake SQL) Expert

Writing highly optimised, complex queries for data transformation and reporting. Debugging and refactoring legacy SQL. Designing stored procedures and user-defined functions. Analysing execution plans to improve query performance. Setting SQL coding standards for the team.

dbt (data build tool) Advanced

Designing and building complex, multi-layered dbt projects from scratch. Implementing custom macros, data quality tests, and documentation. Managing dbt Cloud deployments and CI/CD pipelines. Championing a software engineering mindset for analytics code.

Snowflake Advanced

Designing and implementing data sharing, cloning, and time travel. Optimising virtual warehouse performance and cost. Managing role-based access control (RBAC) and security. Architecting data ingestion patterns and storage strategies.

Git / GitHub Advanced

Managing merge conflicts, performing rigorous code reviews for direct reports, and helping define and enforce the team's Git workflow (e.g., GitFlow, trunk-based development). Ensuring CI/CD best practices are followed for analytics code.

Python (pandas, SQLAlchemy) Advanced

Building robust data pipelines and automation scripts for tasks that are difficult in SQL (e.g., complex API integrations, advanced data cleaning). Using libraries like `pandas` for complex data manipulation and `SQLAlchemy` for programmatic database interaction. Championing Python for advanced analytics.

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 Design & Architecture	Follows existing patterns; proposes minor changes with review.	Designs new data models for specific business areas, reviewed by senior staff.	Leads the design of complex, enterprise-wide data models; makes technical decisions on schema, grain, and SCD types with minimal oversight.
Technical Tooling & Standards	Uses approved tools and follows established coding standards.	Suggests improvements to existing tools/standards; experiments with new features.	Evaluates new tools for specific projects; helps define and refine team coding standards.
Project Prioritisation & Resource Allocation	Works on tasks as assigned by team lead.	Manages own task backlog within a project; flags potential delays.	Manages a workstream's priorities; makes recommendations on task sequencing.
Mentorship & Team Development	Seeks guidance from senior team members.	Provides informal guidance to new joiners on basic tasks.	Formally mentors 1-2 junior developers; provides detailed code reviews and unblocks technical challenges.

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.

Project Delivery Rate

The percentage of major BI workstreams and projects you lead that are delivered on time and meet the agreed-upon technical specifications and business requirements.

Target · 90%+ of projects delivered on schedule and scope.

Leading the build of the new customer lifetime value (CLTV) data model, it was delivered two days early, and all initial requirements were met, resulting in a 100% project delivery rate for that initiative.

Data Model & Dashboard Performance

Average load times for critical dashboards and the query execution speed of core data models. This means making sure our reports don't leave people waiting.

Target · Average dashboard load time under 5 seconds; critical query execution under 10 seconds.

After optimising the Sales Performance dashboard's DAX calculations and underlying Snowflake tables, its average load time dropped from 12 seconds to 4 seconds, directly improving user experience.

Data Quality & Reliability

The number of critical data quality issues or data pipeline failures that impact production reports. We're talking about things that break dashboards or show wrong numbers.

Target · Zero critical data quality incidents or pipeline failures per quarter.

Through proactive monitoring and robust dbt testing, your team prevented a potential finance reporting error caused by a source system change, resulting in zero critical incidents for the quarter.

Technical Debt Reduction

The amount of legacy code or inefficient data models refactored or replaced with modern, scalable solutions. This keeps our platform healthy.

Target · Reduce identified technical debt by 15-20% annually.

You led the migration of three legacy SQL Server stored procedures to a modern dbt model in Snowflake, removing a significant maintenance burden and improving data lineage.

Architectural Soundness & Scalability

How well your designs stand up to new business requirements and increasing data volumes. Are we building for tomorrow, not just today?

Your proposed architectural changes are consistently approved by the Director of BI and Data Engineering. New data sources are integrated smoothly without causing performance bottlenecks. Your data models are rarely refactored due to unforeseen scalability issues. You're often consulted by other teams on their data architecture challenges.

Technical Leadership & Mentorship

Your ability to guide, unblock, and upskill the junior and mid-level developers on your team. Are they learning and growing because of you?

Direct reports consistently meet their development goals and express positive feedback about your guidance. You regularly lead code reviews that genuinely improve code quality. You're seen as the go-to person for complex technical challenges, and you're good at explaining solutions clearly. Team members are visibly improving their technical skills under your wing.

Standardisation & Best Practice Adoption

How effectively you establish and enforce coding standards, data modelling conventions, and development best practices across the BI team.

The team's code is consistently formatted and follows agreed-upon conventions (e.g., dbt style guide). New data models adhere to dimensional modelling principles. You've successfully rolled out new tools or processes (e.g., Git workflow, automated testing) that the team now uses as standard. Your documentation is clear and actually gets used.

Cross-Functional Influence

Your ability to get other teams (like Data Engineering, Product, or business units) on board with your technical recommendations and data governance initiatives.

You're regularly invited to early-stage planning meetings for new data initiatives. Your recommendations on data definitions or reporting approaches are adopted by key business stakeholders. You can successfully mediate disagreements between technical and business teams on data requirements. You're able to push back constructively when requirements are unclear or technically unfeasible, offering viable alternatives.

7Would you like it

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

What people enjoy

Building Robust Systems

You get a real kick out of designing a clean, scalable data model or optimising a slow query to run in seconds. The idea of building something that 'just works' and can handle anything thrown at it is deeply satisfying.

Spending a full day refactoring a messy legacy data pipeline into a beautiful, idempotent dbt model that now runs flawlessly every night, knowing you've made the system fundamentally better.

Solving Complex Data Puzzles

The more ambiguous or challenging a data problem, the more you're drawn to it. You love the process of breaking down a huge, messy data question into manageable pieces and finding the elegant solution.

Successfully reconciling a multi-million-pound revenue discrepancy between two different systems, tracing the issue back to a subtle data entry error that no one else could find.

Empowering Others with Data

You enjoy seeing your dashboards and data models being used to make real business decisions. You're motivated by the idea that your work helps others understand the business better and achieve their goals.

Receiving feedback from the Head of Sales that the new regional performance dashboard you designed helped them reallocate resources and hit their quarterly targets.

Technical Leadership & Mentorship

You thrive on guiding junior developers, sharing your expertise, and seeing them grow. You enjoy setting technical standards and helping the team adopt best practices, knowing you're building a stronger collective.

Successfully mentoring a mid-level developer through their first complex dbt project, helping them overcome challenges and seeing them confidently present their solution to stakeholders.

What frustrates people

Spending 60% of your time on data cleansing and validation because the source systems are a mess, and still getting blamed when the data is wrong.
The 'Just one more thing' stakeholder: the project is 99% done, but the business user keeps adding 'small' requests that push the deadline back and require significant rework.
Being asked to 'build a sales dashboard' and having to drag every single metric, filter, and chart requirement out of stakeholders who 'will know it when they see it.'
Building a sophisticated, interactive dashboard, only to see users immediately click 'Export to CSV' to do their own VLOOKUPs because it's what they're comfortable with.
Explaining to an executive why you can't put 100 columns and 5 years of daily transactional data on a single dashboard and expect it to load in under a second.
Constantly having to tell people why their interpretation of a metric is wrong or that the 'insight' they found is just a data quality issue.
Your entire sprint plan getting derailed by an 'urgent, CEO-level' request for a single number that takes two days to validate, only for it to be forgotten a week later.

What this role does not give you

A perfectly clean data environment with no legacy systems to deal with.
Complete autonomy without needing to justify technical decisions to non-technical people.
A guarantee that every single piece of work you produce will be immediately adopted and used.
A quiet, solitary coding role; you'll be interacting with people constantly.
A static set of requirements; things change, and often quite rapidly.

8Who you work with

This role directly shapes the reliability, performance, and strategic value of our entire BI platform. Your architectural decisions will dictate how quickly we can adapt to new business needs, how much we spend on data infrastructure, and how much trust the business places in our numbers. You're essentially building the brain of our data-driven decision-making.

Inside the business

Director of Business Intelligence
Head of Data Engineering
Product Managers (especially those focused on data products)
Senior Data Analysts
Finance Leadership
Sales Operations Leads
Marketing Directors

Outside the business

Key Technology Vendors (e.g., Snowflake, Power BI)
External Consultants (on specific projects, if applicable)

9What you need before you start

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

A minimum of 5 years of hands-on experience as a Senior Business Intelligence Developer or a similar role, with a proven track record of leading complex projects.
Demonstrable experience in architecting and implementing dimensional data models from conceptualisation to production.
Extensive experience with at least one major cloud data warehouse (e.g., Snowflake, BigQuery, Redshift) and a modern transformation tool (e.g., dbt).
Proven ability to mentor junior developers and lead technical discussions effectively.
A strong portfolio of complex SQL queries, dbt models, and Power BI/Tableau dashboards that showcase your technical depth and design prowess.

10What to practise next

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

Advanced Data Orchestration & Automation

As data pipelines become more complex and real-time needs increase, simply scheduling dbt runs isn't enough. You'll need to integrate dbt with more sophisticated orchestration tools (e.g., Airflow, Dagster) and build robust, observable data workflows.

Directed Acyclic Graphs (DAGs) · Observability & Alerting · Dynamic Scheduling · Containerisation (Docker, Kubernetes)

This month: Take an online course on Apache Airflow or Dagster.
Month 2: Build a small proof-of-concept data pipeline using one of these orchestration tools, integrating a dbt project.
Month 3: Explore how to add robust monitoring and alerting to your orchestrated pipelines.
Month 4: Propose an improved orchestration strategy for one of our critical data pipelines.

Quick win: Start investigating the current orchestration tools we use. What are their limitations? How could they be improved? Read documentation and watch tutorials on Airflow or Dagster.

Real-time Analytics Architecture

The demand for immediate insights is growing. Moving from batch processing to near real-time or streaming analytics will be a key differentiator. You'll need to understand the architectural implications and trade-offs.

Streaming Data Platforms (Kafka, Kinesis) · Materialized Views & Incremental Processing · Lambda/Kappa Architectures · Real-time BI Tools

This month: Research the differences between batch and streaming analytics, and common use cases.
Month 2: Explore how Snowflake's 'Streams and Tasks' or similar features can enable near real-time analytics.
Month 3: Identify one business use case where real-time data would provide significant value.
Month 4: Work with Data Engineering to design a high-level architecture for a real-time data pipeline for that use case.

Quick win: Identify a critical dashboard that currently refreshes daily. How much value would be gained if it refreshed hourly or every 15 minutes? Start thinking about the technical challenges involved.

11Staying current once you are in

What people here do to keep up

Regularly attending industry conferences (e.g., Data + AI Summit, Snowflake Summit) to stay abreast of the latest trends and network with peers.
Contributing to open-source data projects or maintaining a personal portfolio of data solutions on GitHub.
Leading internal 'lunch and learn' sessions to share your expertise and foster a culture of continuous learning within the team.
Participating in online communities (e.g., dbt Slack, Power BI forums) to collaborate and solve complex problems with a wider audience.
Taking advanced courses on data architecture, cloud computing, or specific programming languages (e.g., Python for data engineering).

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration for Data Tasks

Competitors are already using Large Language Models (LLMs) to draft reports in minutes that used to take hours, or to generate complex SQL queries from natural language. Analysts and Leads who figure this out will outproduce their peers 3:1. This isn't just about using ChatGPT; it's about integrating LLMs into our actual data workflows.

Context Windows & Token Limits
Retrieval Augmented Generation (RAG)
Output Validation & Hallucination Detection
Prompt Chaining for Complex Analysis

Data Mesh & Data Fabric Concepts

As organisations grow, centralised data teams often become bottlenecks. Data Mesh (decentralised data ownership) and Data Fabric (integrated data platform) are becoming critical architectural paradigms for scaling data capabilities. As a Lead, you'll need to understand how these impact our BI platform and team structure.

Data as a Product
Decentralised Data Ownership
Self-Serve Data Platform
Interoperability & Governance

13What you’ll use

What you’ll use

Skills this role draws on

Technical

Dimensional Modelling (Kimball)
ELT (Extract, Load, Transform) Design
Data Warehousing Concepts
Query Performance Tuning
Semantic Layer Design
Agile BI Development

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
From Senior BI Developer
2-4 years as a Senior BI Developer
Skills to master
- Moving from owning workstreams to architecting entire solutions, formally mentoring junior staff, and influencing broader technical strategy. You'll need to get really good at translating complex technical concepts for non-technical audiences.
You're ready to move on when
- You're consistently the 'go-to' person for the most complex technical challenges on your team.
- You've informally mentored junior colleagues and they've shown significant growth under your guidance.
- You've successfully led at least 2-3 major BI projects from design to deployment, including data model architecture.
- You're actively contributing to technical discussions beyond your immediate project scope.
2
From Senior Data Analyst (with strong technical skills)
3-5 years as a Senior Data Analyst, plus additional technical upskilling
Skills to master
- Deepening your expertise in data warehousing (dimensional modelling, ELT), advanced SQL, dbt, and cloud data platforms. You'll need to shift from primarily consuming data to building and maintaining the underlying data infrastructure.
You're ready to move on when
- You've built complex SQL-based data marts or transformations that are used by others.
- You're proficient in a BI tool like Power BI or Tableau, not just for reporting, but for data modelling.
- You've taken initiative to learn and apply modern data engineering practices (e.g., dbt, Python scripting).
- You can articulate how data models are designed for scalability and performance.
3
From Data Engineer (with BI focus)
2-3 years as a Data Engineer
Skills to master
- Developing a stronger understanding of business reporting requirements, data visualisation best practices, and semantic layer design. You'll need to bridge the gap between raw data pipelines and actionable business insights, often involving more direct stakeholder interaction.
You're ready to move on when
- You have experience building robust data pipelines and ETL processes.
- You've worked with dimensional models and understand their purpose for analytics.
- You're interested in the 'last mile' of data – how it's consumed and used for decision-making.
- You're comfortable interacting with business users to gather requirements.

15Where this role leads

The long view: This role can lead to running a BI function as a director, heading up a wider data organisation, or moving into a chief data officer role.

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 Lead Business Intelligence Developer 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, worked against Lead Business Intelligence Developer.

Data Analytics Level 5

Applied to your work in Lead Business Intelligence Developer

The objective of this unit is to enable learners to understand and apply data analytics techniques for decision-making. Learners will be able to apply descriptive, predictive, and prescriptive analytic methods, utilising statistical methods, to convert raw data into actionable insights and determine the best course of action.

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 Lead Business Intelligence Developer plan, free

See Your Progress Grow Illustration

Lead Business Intelligence Developer

Dimensional Modelling (Kimball)
ELT (Extract, Load, Transform) Design
Data Warehousing Concepts
Query Performance Tuning
Semantic Layer Design
Agile BI Development

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

Lead Business Intelligence Developer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

BI Manager
2-4 years as a Lead BI Developer

This is a move into formal people management, leading a larger team and focusing more on strategic planning, resource allocation, and stakeholder management.
- Vendor Management Negotiating contracts and managing relationships with external technology providers.
- Organisational Design Structuring the BI team for optimal efficiency and impact.
- Executive Communication Presenting complex BI strategies and outcomes to senior leadership and the board.
Principal BI Architect (Individual Contributor Path)
3-5 years as a Lead BI Developer

This is a deeper dive into technical excellence, becoming the ultimate technical authority and thought leader for the entire BI platform, without formal people management responsibilities.
- Data Governance Framework Design Architecting the technical components of data governance (e.g., metadata management, data catalogue integration).
- Cloud Cost Optimisation (Advanced) Deep expertise in optimising cloud data warehouse costs at an enterprise level.
- Security Architecture Designing and implementing advanced security measures for sensitive data within the BI ecosystem.

Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, as a Lead BI Developer, your plate is always full. You're balancing architecture, coding, mentoring, and endless stakeholder meetings. What if you could reclaim a significant chunk of your week, not by working less, but by working smarter? That's where AI comes in.

We're not talking about AI replacing you; we're talking about AI making you a data superhero. Imagine automating the tedious bits, getting instant code suggestions, and having an intelligent assistant to draft your executive summaries. This isn't future tech; it's here now, and we're building an environment where you can use it to its full potential.

Automated SQL & DAX Generation

Use AI copilots (like GitHub Copilot or dedicated BI AI assistants) to translate complex natural language prompts – 'Show me the 3-month rolling average of sales for our top 10 products by region, excluding returns' – into optimised SQL queries or intricate DAX measures. This includes generating comments and even suggesting performance improvements. You'll spend less time writing boilerplate and more time validating the logic and refining the output.

Proactive Anomaly Detection & Root Cause Analysis

Feed time-series data from critical metrics (e.g., daily active users, conversion rates, ETL job run times) into an AI model. It'll automatically flag statistically significant anomalies and, crucially, suggest potential root causes by correlating with other data points. This means you can prevent surprises at month-end, diagnose issues faster, and focus your forensic skills on the truly novel problems, rather than spotting the obvious.

Intelligent Data Model Documentation

Point an AI tool at your Snowflake schema, dbt project, or Power BI dataset. It automatically generates clear, business-friendly documentation for every table, column, and measure, including descriptions, relationships, and usage examples. This keeps your data dictionary perpetually up-to-date, reduces the burden of manual documentation, and helps your team (and the business) understand the data faster. You'll spend less time writing about data and more time building with it.

Executive Summary & Communication Drafts

After building a complex dashboard or completing a deep dive analysis, feed the key charts, data points, and findings to an AI. It can generate a concise, narrative summary of the key insights, trends, and takeaways in plain English, tailored for senior leadership. This means less time struggling with wording for emails or presentations, and more time ensuring the insights are impactful and well-understood.

Common questions

How do you become a Lead Business Intelligence Developer?

Common routes in include From Senior BI Developer (2-4 years as a Senior BI Developer), From Senior Data Analyst (with strong technical skills) (3-5 years as a Senior Data Analyst, plus additional technical upskilling) and From Data Engineer (with BI focus) (2-3 years as a Data Engineer). Times vary with prior experience.

Where can a Lead Business Intelligence Developer progress to?

This role can lead on to BI Manager (2-4 years as a Lead BI Developer) and Principal BI Architect (Individual Contributor Path) (3-5 years as a Lead BI Developer), depending on the skills you build.

What new skills matter most for a Lead Business Intelligence Developer?

Increasingly, Prompt Engineering & LLM Integration for Data Tasks and Data Mesh & Data Fabric Concepts. 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 Lead Business Intelligence Developer 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.

Lead Business Intelligence Developer

Where should we send it?

Two short quizzes about you

Will AI change your work as a Lead Business Intelligence Developer?

What's rising in its place

Prompt Engineering & LLM Integration for Data Tasks

Data Mesh & Data Fabric Concepts

Skills this role draws on

Technical

How you actually get there, here

From Senior BI Developer

From Senior Data Analyst (with strong technical skills)

From Data Engineer (with BI focus)

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

BI Manager

Principal BI Architect (Individual Contributor Path)

Where AI is starting to help

Automated SQL & DAX Generation

Proactive Anomaly Detection & Root Cause Analysis

Intelligent Data Model Documentation

Executive Summary & Communication Drafts

Common questions

Want a read on where you stand?