The goal is to move beyond static reporting and enable faster, more scalable decision-making across Operations .

What You’ll D

**o

Build the Operational Data Lay**

• erDesign and maintain scalabl e DBT models and SQL pipelin es that transform raw operational data into clean, reliable analytics layer
• s.Establish clea r metric definitions and data mode ls so operational teams can trust and reuse the same datasets across different analyse
• s.Develop a single operational analytics lay er that integrates data across multiple systems including customer support platforms, risk systems, QA tooling, and payment

**s.

Enable Self-Serve Analyt**

• icsDesign systems that allow Operations teams to explore data independently without relying on manual report i
• ng.Levera ge AI-assisted analytics tools (e.g., Claude or similar LLM workflo ws) to enable teams to query data, generate insights, and explore trends more efficient
• ly.Build internal tooling and workflows that make operational da ta easier to access, understand, and anal y

**se.

Analytical Deep Dives & Insight Genera**

• tionGo beyond reporting to identify operational inefficiencies, behavioural trends, and improvement opportuni t
• ies. Use Python and SQL to conduct deeper analysis and create clear visualisations that help stakeholders understand complex operational dynam
• ics.Produce structured analyses on topics such
• as:SLA performance and operational bottlen
• ecksFraud and financial crime tr
• endsCustomer support effici
• encyComplaint and vulnerability patt
• ernsAgent productivity and QA perfor

**manc

Data Quality & Inte**

• grityEnsure data pipelines and analytical models are accurate, reliable, and sca l
• able.Proactively identify data discrepancies or gaps and improve the robustness of operational data pipel
• ines.Implement processes that en sure consistent metric definitions and version-controlled analytics l

**ogic.

Stakeholder Enga**

• gementTranslate operational questions into data models, analyses, and insights that drive decision- m
• aking.Proactively identify opportunities where data and analytics can improve operational perfor

**mance.

What We’re Look**

**ing For

Es**

• sential:1+ years experi ence in analytics engineering, BI, or data analyti cs roles in SQL-heavy envir
• onments.Strong experien ce with SQL and familiarity with DBT, including building and maintaining scalable data
• models.Good understan ding of data modelling, metric standardisation, and analytical best p r
• actices.Ability to translate complex da ta into clear insights for both technical and non-technical stak e

holders.

• DesirableExperience work ing with operational datasets (customer support, collections, fraud/fincrime, QA, com
• plaints).Exp osure 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 da
• shboards.Familiar ity with modern da ta stacks (DBT, Superset/Preset, Snowflake/BigQuer
• y, etc.).Experien ce using Python for analysis and visu alisation (e.g., Pandas, matplotlib, plotly, seabor
• n, etc.).Exper ience in regulated env ironments or with regulatory reporting requ

**irements.

Intervi**

1. ew ProcessQuick call with a Recruite
2. r (30 min)Technical Interview - (SQL + analytical thinkin
3. g (60 min)Competency interview with the hiring manage
4. r (30 min)Final Interviews with Senior Stakeholders

(2x30 min)