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For Companies At a Glance
- Tasks: Analyse operational data and optimise processes for better efficiency.
- Company: Join a leading fintech company focused on modern investing.
- Benefits: Remote work, flexible hours, and opportunities for growth.
- Why this job: Make a real impact by improving operational workflows with data insights.
- Qualifications: 3+ years in data analysis, strong SQL skills, and experience with BI tools.
- Other info: Collaborative culture with a supportive team and transparent communication.
The predicted salary is between 36000 - 60000 £ 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 have built a strong foundation, have over £2 billion invested, award-winning service, and a passionate team, now we are ready to scale.
About The Role
We are looking for an Operations Data Analyst to join our Analytics team. You will be working on strengthening and optimising our back-office operations through high-quality data analysis, operational reporting, and process redesign across key retail investment workflows. This role is ideal for someone who is analytically rigorous, detail-oriented, and motivated by improving operational efficiency through data. You enjoy reconstructing processes from operational data, identifying control gaps, and turning insights into practical improvements.
You will work closely with Operations, Product, Engineering, and Compliance stakeholders and have the autonomy to own analytical solutions end to end — from requirements gathering through to dashboard delivery and process improvement.
What You Will Do
- Analyse operational data across KYC/AML, trading and settlement, corporate actions, and cash reconciliations
- Map end-to-end operational processes and create clear process flow documentation
- Identify inefficiencies, control gaps, and data discrepancies within operational workflows
- Build and maintain dashboards using BI tools to monitor operational performance and risk metrics
- Develop and automate recurring operational reports using SQL and Excel
- Write SQL queries (including joins) to extract, validate, and reconcile data from multiple sources
- Partner with stakeholders to translate business requirements into analytical specifications
- Redesign data-intensive processes to improve accuracy, scalability, and control
- Contribute to data quality initiatives and root-cause analysis
What We Are Looking For
- 3+ years' experience in a Data Analyst, Operations Analyst, or similar role, with exposure to operational processing or operational data
- Experience within financial services, ideally in a retail investment or fintech environment
- Strong working knowledge of SQL (including joins)
- Proven experience using BI tools (e.g. Metabase, Tableau, Power BI) and building production-ready dashboards
- Advanced Excel expertise, including complex formulas, data manipulation, and reconciliation work
- Experience performing data validation and operational root-cause analysis
- Strong stakeholder management skills, with the ability to communicate clearly with both technical and non-technical teams
- Structured, ownership-driven approach to problem-solving
- English fluency (C1 level and above) — we are a UK-based company, and the role involves regular communication with the team
Nice to Have
- Experience with ETL processes or exposure to data warehousing concepts
- Working knowledge of Python for data analysis
How We Work
We are a lean, fast-moving team that values clarity, ownership, and transparency. You will have the freedom to experiment, the responsibility to follow through, and the backing of a team that values clear thinking and open dialogue. We believe in solving problems at the root, not just treating the symptoms.
What We Offer
- Impact from day one: You will take on meaningful work from the start, tackling real challenges that drive the stability, efficiency, and growth of our business.
- Room to grow: As we scale, you will have opportunities to expand your responsibilities, influence how we work, and help shape our long-term direction.
- Transparent and open culture: We share decisions openly, keep communication channels clear, and encourage collaboration across every part of the business.
- Supportive, Experienced Team: Work alongside talented professionals who are experts in their fields — smart, driven, and generous with their knowledge.
- Remote first and flexible: Work in the way that best suits you. We focus on results, not rigid hours, and trust you to manage your time effectively.
Our Hiring Process
- Introductory call with our Talent team
- Competency interview focused on your experience
- Gamified cognitive assessment to understand how you think and problem-solve
- Interview with a hiring manager to explore alignment with our culture, values, and strategic direction
Operations Data Analyst in London employer: InvestEngine
Contact Detail:
InvestEngine Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Operations Data Analyst in London
✨Tip Number 1
Network like a pro! Reach out to current employees at InvestEngine on LinkedIn. A friendly message can go a long way in getting insider info and maybe even a referral.
✨Tip Number 2
Prepare for the interview by brushing up on your SQL skills and BI tools. Be ready to showcase how you've used data to solve real operational problems in your past roles.
✨Tip Number 3
Showcase your analytical mindset! During interviews, share specific examples of how you've identified inefficiencies and implemented improvements in previous jobs.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Operations Data Analyst in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Operations Data Analyst role. Highlight your experience with SQL, BI tools, and any relevant financial services background. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data analysis and how you can contribute to our team. Be sure to mention specific projects or experiences that relate to operational efficiency.
Showcase Your Analytical Skills: In your application, don’t just list your skills—show us how you've used them! Include examples of how you've improved processes or solved problems using data analysis. We love seeing real-world applications of your expertise.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our awesome team!
How to prepare for a job interview at InvestEngine
✨Know Your Data Inside Out
As an Operations Data Analyst, you'll be knee-deep in data. Make sure you brush up on your SQL skills and understand how to write complex queries. Be prepared to discuss specific examples of how you've used data to identify inefficiencies or improve processes in your previous roles.
✨Showcase Your Analytical Mindset
InvestEngine is looking for someone who can think critically about operational workflows. Prepare to talk about a time when you mapped out a process or redesigned a workflow based on your analysis. Highlight your attention to detail and how it led to tangible improvements.
✨Familiarise Yourself with BI Tools
Since you'll be building dashboards and reports, make sure you're comfortable discussing the BI tools you've used, like Tableau or Power BI. Bring examples of dashboards you've created and be ready to explain the insights they provided and how they impacted decision-making.
✨Communicate Clearly with Stakeholders
You'll need to work closely with various teams, so practice explaining technical concepts in simple terms. Think of examples where you've successfully collaborated with both technical and non-technical stakeholders to achieve a common goal. This will show that you can bridge the gap between data and business needs.