Product & Platform Delivery:
•Plan and lead the implementation of analytics products on the Medha Analytics Platform.
•In partnership with the data engineering team, product team, and quality assurance team, prioritise and lead product feature development.
•Define, communicate, and proactively analyse bottlenecks, and ensure adherence to product delivery SLAs.
•Propose multiple potential solutions for complex or new problems or processes, and make objective recommendations as to which direction should be taken.
•Provide transparency and recommendations on project trade-offs, and provide dependency coordination.
•Perform with a wide degree of latitude on creative solutions.
•Continually assess and integrate internal & external customer feedback and business metrics.
•Stay on top of market trends to determine new or enhanced product capabilities that positively impact business objectives.
Team Leadership & Analyst Management:
•Directly supervise the Business/Data Analyst team (Analysts and Senior Analysts), who deliver analytics models, dashboards, and AI agent/bot automation for the business.
•Act as the bridge between the product and the analyst team — break down product requirements into well-scoped tasks and set priorities for analysts to execute against.
•Review analyst deliverables (dashboards, models, KPIs/metrics, AI agents and bots) for technical quality, accuracy, and adherence to delivery SLAs before they reach stakeholders.
•Plan team capacity and allocate work across analysts and senior analysts against delivery priorities and SLAs.
•Coach and mentor analysts on technical growth (SQL, Python, BI tooling, AI/GenAI automation and bot development).
•Remove blockers and resolve escalations on behalf of the team, coordinating with data engineering, QA, and other functional/technical teams.
Candidate Requirements:
Education:
•BE / B.Tech in Computer Science or a related technical discipline, or its equivalent.
•PGDM from a leading management institute (good to have).
Experience:
•6 years' experience in a technical Product Management team, working very closely with analysts and database teams, or as an individual contributor.
•A minimum of 4 years of experience in the analytics tech stack, Data Transformation and Data Visualisation, AI Automations, Data Science tools, and Gen AI frameworks.
•Experience directly managing or leading a team of data/business analysts is a plus — this role owns people management for the analyst team, not just project delivery.
Key Skills:
•Ability to balance hands-on technical review with delegation — knows when to guide analysts versus when to step in directly.
•Demonstrated data-driven decision-making capabilities, with strong analytical skills to influence product decisions.
•Experience taking direction and input from multiple sources and creating a shared product vision.
•Demonstrated ability to work on a diverse scope of projects requiring detailed analysis, creative/practical problem-solving, and sound judgment.
•Project management experience on analytics/data intelligence projects, including capacity planning and workload allocation across a team.
•Strong communication skills.
Technical Skills:
•Hands-on experience with analytics product implementation, data analysis, implementation solutioning, and data governance.
•Comfortable working with Big Data technologies, building and designing products that utilise both real-time and batch data streams at scale.
•Ability to write and validate SQL/Python queries, and to ensure data is validated — enough depth to review and sanity-check analysts' own SQL/Python work.
•Ability to understand ETL structures and concepts.
•Ability to understand data visualisation structures and concepts, and familiarity with BI/dashboarding platforms (Power BI, Tableau, Qlik) to review analyst-built dashboards for quality, consistency, and best practices.
•Working knowledge of AI/GenAI tooling and conversational/non-conversational bot & AI agent development — sufficient to review, guide, and unblock analysts building automation solutions, not just consume the output.
•Understanding of the technical architecture of complex data products.
