Senior Data Analyst

Responsibilities

• Lead Data Exploration & Insight Generation – Drive deep analysis of user behaviour, product performance, and operational trends to uncover key drivers of business outcomes. Proactively identify high‑impact opportunities and generate actionable insights that shape product and strategic decisions.
• Own Anomaly Detection & Root Cause Analysis – Independently identify anomalies, emerging patterns, and performance shifts across key metrics. Lead root‑cause investigations and clearly communicate findings to cross‑functional stakeholders.
• Build Scalable Reporting & Data Infrastructure – Design, develop, and own robust dashboards, reporting frameworks, and automated data pipelines. Ensure data accessibility, consistency, and reliability for stakeholders across the organization.
• Strategic Decision Support – Partner closely with product, operations, and leadership teams to translate complex analytical findings into clear, data‑driven recommendations. Influence roadmap prioritization and business strategy.
• Define & Evolve Core Metrics – Lead the development and refinement of KPIs and success metrics. Ensure alignment with business objectives and establish measurement frameworks to track performance effectively.
• Champion Experimentation & Modeling – Design, execute, and analyze experiments (A/B tests, cohort analysis, impact assessments). Contribute to predictive modeling and advanced analytics initiatives in collaboration with data science teams.
• Drive Best Practices & Documentation – Establish and maintain high standards for documentation, reproducibility, and analytical rigor. Promote best practices in data governance, methodology, and reporting.
• Mentorship & Continuous Improvement – Support and mentor junior analysts, helping to elevate team capabilities. Continuously expand your own technical and product skillset while driving analytical excellence across the team.

• 3‑6+ years of experience in analytics, data, product, or operations roles
• Advanced SQL proficiency for querying, transforming, and optimizing large datasets
• Strong analytical thinking with a structured, hypothesis‑driven approach to problem solving
• Excellent communication skills – able to translate complex analyses into clear, compelling narratives for both technical and non‑technical audiences
• Proven ability to own projects end‑to‑end and influence stakeholders
• Experience automating workflows and improving data processes at scale
• Strong knowledge of financial services, product analytics, and data‑driven decision‑making

• Proficiency in Python (or similar) for data analysis and manipulation
• Solid understanding of experimentation design, statistical concepts, and data modeling fundamentals

• Hands‑on experience with modern BI tools (e.g., Looker, Metabase, Power BI, Tableau)
• Strong understanding of business performance metrics and growth levers
• Experience in product, financial, or fraud analytics
• Exposure to machine learning workflows or advanced analytics use cases