Lead Data Analyst

Role Overview

Power BI, SQL, and Excel

Key Responsibilities

• Own and lead the data analytics charter, ensuring accuracy, accessibility, and usability of all business-critical data.
• Develop

  scalable dashboards and analytical frameworks

  in Power BI for leadership, product, and operations teams.
• Collate, clean, and unify large datasets from

  AWS (Redshift/S3), Metabase, App analytics, and internal tools

  .
• Partner with business teams to translate high-level questions into clear analytical workflows and insight-driven outputs.
• Build advanced Excel models, Power BI data models, and automated reporting pipelines.
• Identify trends, diagnose problem areas, and deliver

  actionable insights

  to improve funnel performance, product adoption, user behavior, credit outcomes, and operational efficiency.
• Establish strong data governance practices, ensure data quality, and improve data lineage across systems.
• Work closely with engineering and product teams to support event tracking, instrumentation, and data availability.
• Evaluate analytical tools and recommend improvements in data stack and visualization framework.
• Present insights and recommendations to leadership in a clear, structured, and business-focused manner.

Required Skills & Competencies

• Advanced proficiency in Power BI

  (data modelling, DAX, performance optimisation, large dataset handling).
• Strong command of

  SQL

  (joins, window functions, optimisation, handling large tables).
• High proficiency in

  Excel

  (complex formulas, pivot modelling, automation).
• Experience working with data stored across

  AWS (Redshift, S3), Metabase, app analytics tools

  , or similar multi-source environments.
• Strong business acumen with the ability to convert data into actionable insights and strategic recommendations.
• Experience managing and leading analytical projects end-to-end; ability to work independently with minimal guidance.
• Understanding of ETL processes, data warehousing concepts, and BI best practices.
• Strong problem-solving, communication, and stakeholder management skills.

Good to Have

• Experience in

  FinTech, Credit, Collections, Lending, or BFSI analytics

  .
• Exposure to predictive modelling, customer segmentation, or churn/propensity models (Python/R optional but beneficial).
• Understanding of product analytics and event-based tracking frameworks (Mixpanel, Amplitude, GA4, etc.).
• Ability to mentor junior analysts and establish analytics processes.

Qualification

• Bachelor’s/Master’s in Data Science, Statistics, Computer Science, Engineering, Finance, or Mathematics.