Data Analyst

Roles and Responsibilities:\n- Work closely with Product Managers to provide data-driven inputs on product performance, user behaviour, and growth opportunities - translating findings into clear, actionable recommendations.\n- Analyse product funnels, cohorts, trends, LTV, DAU/MAU, retention, and user behavior to support prioritisation and decision-making.\n- Define and own stakeholder requirements for customer usage metrics, KPIs, and reporting processes; build and maintain dashboards and reports that surface meaningful signals.\n- Obsessively study customer usage data to proactively identify opportunities for driving higher engagement, improved retention, product improvements, and monetisation levers.\n- Design experiments and analyse A/B tests to measure feature impact and inform product decisions.\n- Design and maintain data marts, dimensional models, and semantic layers that enable self-serve analytics across product and business teams; enforce schema standards and documentation practices.\n- Leverage AI tools effectively and responsibly in day-to-day analytical workflows for better efficiency and maintaining accuracy standards before sharing results with stakeholders.\n- Coordinate independently with cross-functional teams (Product, Engineering, Marketing, Business) and drive fast-paced execution with accountability.\n\nQualifications:\n- B.Tech/BE from a premier institute (IITs/BITS/NITs or equivalent).\n- 5-8 years of relevant experience in Product Analytics or Business Analytics, preferably within a consumer internet / product-tech B2C company (fintech, e-commerce, online marketplaces, etc.).\n- Prior experience working with high-scale consumer products and large user behaviour datasets is a strong advantage.\n\nSkills Required:\n- Product Analytics & BI Tools: Hands-on experience with Amplitude (or similar product analytics platforms such as Mixpanel) for event-level analysis, funnel tracking, and behavioral segmentation. Proficiency with PowerBI or equivalent BI tools for dashboard development.\n- Experimentation & A/B Testing: Practical experience designing, running, and interpreting A/B tests and product experiments - understanding test setup, success metrics, statistical significance, and translating results into recommendations.\n- SQL & Python: Strong SQL skills for complex querying, aggregation, and data transformation. Python proficiency for data wrangling, analysis, and automation (pandas, NumPy).\n- Data Modelling & Schema Design: Working knowledge of data warehouse concepts - star/snowflake schemas, data marts and dimensional modelling. Ability to work with data engineering teams to define schemas and ensure analytical reliability.\n- AI-Augmented Workflows: Demonstrated ability to use AI tools accurately and efficiently in analytical work - including validating AI outputs, spotting hallucinations, and maintaining rigorous standards before sharing results.\n- Excel: Strong MS Excel skills for ad hoc analysis and stakeholder-facing deliverables.\n- Excellent problem-solving skills, strong oral and written communication, and the ability to present complex data clearly to non-technical audiences.\n- Self-motivated, personally accountable, and comfortable working under pressure in a dynamic environment.\n- Understanding of Machine Learning concepts (classification, regression, clustering, propensity modelling) and ability to collaborate with or interpret outputs from data science teams.