Junior Analytics Engineer
UK Visa Sponsorship Analytics
Analytics are greyed out due to low classification confidence (37.0%).
Occupation Type
Telecoms and related network installers and repairers
Occupation Code Skill LevelMedium Skilled
Sponsorship Salary Threshold
£41,700
(£21.38 per hour)
Standard minimum applies
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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
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, and self-serve data tools
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
- Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo
- Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting
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, 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
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.
- Establish clear metric definitions and data models so operational teams can trust and reuse the same datasets across different analyses.
- Develop a single operational analytics layer that integrates data across multiple systems including customer support platforms, risk systems, QA tooling, and payments.
- 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:
- SLA performance and operational bottlenecks
- 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.
- Translate operational questions into data models, analyses, and insights that drive decision-making.
- Proactively identify opportunities where data and analytics can improve operational performance.
Essential
- 1+ years experience in analytics engineering, BI, or data analytics roles in SQL-heavy environments.
- Strong experience with SQL and familiarity with DBT, including building and maintaining scalable data models.
- Good 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 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.).
- Experience using Python for analysis and visualisation (e.g., Pandas, matplotlib, plotly, seaborn, etc.).
- Experience in regulated environments or with regulatory reporting requirements.
- Quick call with a Recruiter (30 min)
- Technical Interview - (SQL + analytical thinking (60 min)
- Competency interview with the hiring manager (30 min)
- Final Interviews with Senior Stakeholders (2x30 min)
- The opportunity to scale up one of the world’s most successful fintech companies.