Analytics Engineer (Operations Analytics & Insights)
UK Visa Sponsorship Analytics
Analytics are greyed out due to low classification confidence (36.0%).
Occupation Type
Metal working machine operatives
Occupation Code Skill LevelIneligible
Sponsorship EligibilityIneligible for Sponsorship
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Description
About Lendable
Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start:
We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.
Join us if you want to
Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start:
- One of the UK’s newest unicorns with a team of just over 700 people
- Among the fastest-growing tech companies in the UK
- Profitable since 2017
- Backed by top investors including Balderton Capital and Goldman Sachs
- Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot)
We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.
Join us if you want to
- Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1
This role sits at the intersection of data engineering, analytics, and operational insight. You will build the underlying data models that power operational reporting, while also helping teams unlock insights through AI-assisted analytics, Python-based analysis, and self-serve data tools.
The goal is to move beyond static reporting and enable faster, more scalable decision-making across Operations.
What You’ll Do
Build the Operational Data Layer
- Design and maintain scalable DBT models and SQL pipelines that transform raw operational data into clean, reliable analytics layers.
- Design systems that allow Operations teams to explore data independently without relying on manual reporting.
- Leverage AI-assisted analytics tools (e.g., Claude or similar LLM workflows) to enable teams to query data, generate insights, and explore trends more efficiently.
- Build internal tooling and workflows that make operational data easier to access, understand, and analyse.
- Go beyond reporting to identify operational inefficiencies, behavioural trends, and improvement opportunities.
- Use Python and SQL to conduct deeper analysis and create clear visualisations that help stakeholders understand complex operational dynamics.
- Produce structured analyses on topics such as:
- Fraud and financial crime trends
- Customer support efficiency Complaint and vulnerability patterns
- Agent productivity and QA performance
- Ensure data pipelines and analytical models are accurate, reliable, and scalable.
- Proactively identify data discrepancies or gaps and improve the robustness of operational data pipelines.
- Implement processes that ensure consistent metric definitions and version-controlled analytics logic.
- Mentor junior analysts on SQL modelling, analytical thinking, and best practices in data modelling and insight generation.
- Help build scalable workflows so the Operations Analytics function can grow efficiently.
- Act as a key analytics partner to Operations leadership, including the COO and senior stakeholders.
- Translate operational questions into data models, analyses, and insights that drive decision-making.
- Proactively identify opportunities where data and analytics can improve operational performance.
What We’re Looking For
Essential
Essential
- 3+ years experience in analytics engineering, BI, or data analytics roles in SQL-heavy environments.
- Strong experience with SQL and DBT, including building and maintaining scalable data models.
- Experience using Python for analysis and visualisation (e.g., Pandas, matplotlib, plotly, seaborn, etc.).
- Strong understanding of data modelling, metric standardisation, and analytical best practices.
- Ability to translate complex data into clear insights for both technical and non-technical stakeholders.
- Experience mentoring analysts or contributing to team development.
- Experience working with operational datasets (customer support, collections, fraud/fincrime, QA, complaints).
- Exposure to AI-assisted analytics workflows (e.g., Claude, GPT, or similar tools used to enhance analysis or self-serve data access).
- Experience building internal data tools or analytical workflows beyond traditional dashboards.
- Familiarity with modern data stacks (DBT, Superset/Preset, Snowflake/BigQuery, etc.).
- Recruiter call
- Technical Interview (SQL + analytics thinking)
- Task Debrief / Technical Interview
- Final Interviews with Senior Stakeholders
- The opportunity to scale up one of the world’s most successful fintech companies.
- Best-in-class compensation, including equity.
- You can work from home every Monday and Friday if you wish - on the other days, those based in the UK come together IRL at our Shoreditch office in London to be together, build and exchange ideas.
- Enjoy a fully stocked kitchen with everything you need to whip up breakfast, lunch, snacks, and drinks in the office every Tuesday-Thursday.
- We care for our Lendies’ well-being both physically and mentally, so we offer coverage when it comes to private health insurance
- We're an equal-opportunity employer and are looking to make Lendable the most inclusive and open workspace in London