Analytics Engineer, Lendable

• 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

Desirable

• We’re looking for a hands-on Analytics Engineer to lead the development of data infrastructure and analytical capabilities across our Operations teams (Customer Service, Financial Support, Fraud, FinCrime, Complaints, and QA)
• 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
• You'll 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
• 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

• Recruiter call
• Technical Interview (SQL + analytics thinking)
• Task Debrief / Technical Interview
• Final Interviews with Senior Stakeholders