At a Glance
- Tasks: Join our Analytics team to drive data governance and compliance while automating processes.
- Company: InvestEngine, a modern investment platform with a passionate team and award-winning service.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic environment with opportunities to propose new tools and ways of working.
- Why this job: Make a real impact by shaping analytics and using cutting-edge technology.
- Qualifications: Experience in data analysis, strong SQL skills, and a passion for automation.
The predicted salary is between 45000 - 55000 £ per year.
About InvestEngine: InvestEngine is everything the modern investor should need. Unbeatable value, market-leading automation, and built for easy, long-term investing. We’ve built a strong foundation, have over £2 billion invested, award-winning service, and a passionate team, now we’re ready to scale.
Role Overview: As an Analyst, you will play a key role within the Analytics team, initially focused on data governance and compliance — spanning topics such as Consumer Duty, CASS, KYC/AML and data protection — with the expectation of broadening into general analytics support across the business over time. You will work closely with stakeholders across Compliance, Risk, Operations, Product and Commercial to translate governance and regulatory requirements into clear, well-documented data controls, reports and dashboards — using a modern, automated tech stack.
This role is best suited to an analytically minded person with an engineering mindset: someone who instinctively looks to automate and simplify rather than accept a manual, spreadsheet-driven process, and who is confident proposing new ways of working, new team structures, or new technology when the current approach isn't fit for purpose. You should be comfortable with ambiguity in a growing analytics function, and want to work in a modern, automation- and AI-enabled way rather than through manual, ad hoc reporting.
Key Responsibilities
- Governance & Compliance: Support the design and rollout of a data governance framework: data ownership, definitions, lineage and retention policies. Partner with Compliance, Risk and Legal stakeholders to translate regulatory requirements (e.g. Consumer Duty, CASS, KYC/AML, GDPR) into concrete data controls, monitoring and reporting. Maintain and evolve the analytics glossary, data catalogue and semantic layer so compliance-related metrics are clearly defined, consistent and auditable. Support internal and external audit requests with well-documented, reproducible analysis.
- Modern Analytics & Automation: Build and maintain data transformations and models using SQL and dbt, following version-controlled, tested and documented practices. Automate recurring reports, reconciliations and alerts to reduce manual effort and strengthen controls. Use AI-enabled tools (e.g. Claude, Copilot) responsibly to accelerate analysis, coding, QA and documentation, within agreed data-handling and compliance guardrails. Contribute to modern software-engineering practices for analytics: version control (git), peer review of dbt models/queries, and lightweight CI/CD where relevant.
- Ways of Working & Continuous Improvement: Default to automating and simplifying: question manual, spreadsheet-driven or one-off processes rather than just repeating them. Propose new tools, architectures, or ways of working where the current setup is holding the team back — this role is expected to shape the analytics function's approach, not just operate within it. Bring an engineering-influenced approach to analytics work: reusable, testable, version-controlled, rather than one-off scripts or manual spreadsheets.
- Reporting & Dashboards: Build and maintain dashboards and reports using modern BI tools. Ensure metrics are correctly defined, documented, and consistent with the data catalogue and semantic layer. As the role broadens, support wider analytics team with ad hoc analysis outside governance and compliance.
- Data Quality & Documentation: Work with the Chief Data Officer to ensure data accuracy, reconciliation and quality. Contribute to data documentation, definitions and the analytics glossary. Raise data quality issues and support root-cause analysis.
- Stakeholder Collaboration: Work closely with business and compliance stakeholders to understand requirements and analytical needs. Clearly communicate insights and findings in a structured, accessible way. Support decision-making by translating complex analysis and regulatory detail into clear narratives.
Requirements & Skills
- Experience in a Data Analyst, Business Analyst, Compliance Analyst or similar role.
- Strong SQL skills; hands-on experience with dbt, or a strong aptitude and willingness to learn modern transformation tooling quickly.
- Familiarity with data governance and one or more relevant regulatory/compliance topics (e.g. Consumer Duty, CASS, KYC/AML, GDPR, Market Abuse Regulation, data retention) is desirable.
- An engineering mindset — a demonstrated habit of automating, simplifying, or redesigning a manual process rather than living with it, even where that means suggesting a new tool or way of working.
- Experience or strong interest in using AI tools to enhance analytical workflows — e.g. AI-assisted coding, prompt engineering, automation of repetitive tasks.
- Comfortable with version control (git) and other modern, engineering-influenced ways of working.
- Experience with modern BI tools (e.g. Metabase, Lightdash, Omni, Looker).
- Strong analytical thinking and problem-solving ability.
- Clear written and verbal communication skills, including with non-technical, compliance-focused stakeholders.
- Confidence to challenge the status quo and propose alternatives, backed by data and sound reasoning.
- Experience in fintech or financial services is desirable but not essential.
What Success Looks Like
- A trusted, well-documented and automated data governance reporting suite covering the business's key data domains, including Consumer Duty, CASS and KYC/AML.
- The use of a modern, automated reporting stack (SQL/dbt) to replace manual, ad hoc processes.
- At least one meaningful new way of working, tool, or structural change proposed and adopted by the team.
- Effective, appropriate use of AI tools to speed up delivery without compromising data governance.
- Strong stakeholder relationships and increasing autonomy as the role broadens beyond governance and compliance.
Our Hiring Process
- Introductory call with our Talent team
- Gamified cognitive assessment to understand how you think and problem-solve
- Competency interview focused on your experience in operational risk and controls (may include a short task or case study)
- Senior leadership interview to explore alignment with our culture, values, and strategic direction
Data Analyst in London employer: InvestEngine
InvestEngine is an exceptional employer, offering a unique opportunity to shape the strategic direction of a fast-growing UK investment platform. With a transparent and collaborative culture, employees benefit from hybrid working arrangements and the chance to lead a dedicated team while directly partnering with the CEO. The company prioritises employee growth through hands-on leadership roles and the freedom to design and implement innovative operating models.
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We think you need these skills to ace Data Analyst in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
