Junior Analytics Engineer

Analytics Engineer – Operations Analytics & Insights

We’re looking for a Junior Analytics Engineer to support 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, 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.

• 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.

Enable Self-Serve Analytics

• 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.

Analytical Deep Dives & Insight Generation

• 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

Data Quality & Integrity

• 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.

Stakeholder Engagement

• 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:

• 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.

Desirable

• 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.

Interview Process

1. Quick call with a Recruiter (30 min)

2. Technical Interview - (SQL + analytical thinking (60 min)

3. Competency interview with the hiring manager (30 min)

4. Final Interviews with Senior Stakeholders (2x30 min)