Data Analyst - 2 (3-5 years)

Roles and Responsibilities:

• Work closely with Product Managers to provide data-driven inputs on product performance, user behaviour, and growth opportunities - translating findings into clear, actionable recommendations.
• Analyse product funnels, cohorts, trends, LTV, DAU/MAU, retention, and user behavior to support prioritisation and decision-making.
• Define and own stakeholder requirements for customer usage metrics, KPIs, and reporting processes; build and maintain dashboards and reports that surface meaningful signals.
• Obsessively study customer usage data to proactively identify opportunities for driving higher engagement, improved retention, product improvements, and monetisation levers.
• Design experiments and analyse A/B tests to measure feature impact and inform product decisions.
• 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.
• Leverage AI tools effectively and responsibly in day-to-day analytical workflows for better efficiency and maintaining accuracy standards before sharing results with stakeholders.
• Coordinate independently with cross-functional teams (Product, Engineering, Marketing, Business) and drive fast-paced execution with accountability.

Qualifications:

• B.Tech/BE from a premier institute (IITs/BITS/NITs or equivalent).
• 35 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.).
• Prior experience working with high-scale consumer products and large user behaviour datasets is a strong advantage.

Skills Required:

• 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.
• 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.
• SQL & Python: Strong SQL skills for complex querying, aggregation, and data transformation. Python proficiency for data wrangling, analysis, and automation (pandas, NumPy).
• 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.
• 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.
• Excel: Strong MS Excel skills for ad hoc analysis and stakeholder-facing deliverables.
• Excellent problem-solving skills, strong oral and written communication, and the ability to present complex data clearly to non-technical audiences.
• Self-motivated, personally accountable, and comfortable working under pressure in a dynamic environment.

Good to Have:

• Understanding of Machine Learning concepts (classification, regression, clustering, propensity modelling) and ability to collaborate with or interpret outputs from data science teams.