Head of Data Engineering

📋 Description

• • Own and evolve Jiostar’s data engineering strategy end-to-end, translating the company’s explosive growth in content and viewership into a resilient, future-proof data platform that can ingest, process, and serve petabyte-scale data with sub-second latency.
• • Guarantee data quality, availability, and accessibility across every business unit—from content recommendation engines that delight 750 million weekly viewers to executive dashboards that steer billion-dollar content investments—by defining and enforcing clear SLAs, lineage, and ownership models.
• • Drive relentless cost governance: architect cloud-native pipelines (Snowflake, Databricks, Kafka, Airflow, dbt) that scale elastically with IPL finals-level traffic yet shrink during off-peak hours, delivering measurable savings without compromising performance.
• • Build, mentor, and retain a world-class team of 25–40 data engineers, instilling a culture of curiosity, psychological safety, and customer obsession; identify and grow the next generation of staff and principal engineers who will become the technical and cultural backbone of Jiostar.
• • Act as the connective tissue between technical and non-technical stakeholders: translate C-suite questions (“Which genre drives the highest ARPU uplift?”) into backlog items, and conversely explain why a streaming ETL refactor will unlock 30 % faster experimentation cycles for marketing.
• • Operationalize ML and AI at scale: partner with data scientists to productionize real-time personalization models, churn-prediction pipelines, and computer-vision metadata extraction—ensuring feature stores, model registries, and A/B testing frameworks are first-class citizens in the data stack.
• • Run and continuously improve core data programs—ETL/ELT, data observability, metadata governance, privacy compliance (GDPR, India DPDP Act)—with an automation-first mindset that turns manual toil into self-service tooling.
• • Champion data democratization: roll out intuitive, governed self-service analytics layers so that product managers, content strategists, and ad-ops teams can answer their own questions without opening a Jira ticket.
• • Stay ahead of the technology curve: evaluate emerging tools (e.g., lakehouses, data contracts, streaming OLAP) through rapid POCs, but maintain a pragmatic filter—adopting only what demonstrably accelerates business value.
• • Communicate a rolling 12- to 18-month roadmap that balances quick wins (reduce dashboard load times by 50 % in Q2) with strategic bets (real-time ad-insertion telemetry by FY26), securing executive buy-in and budget.
• • Foster an inclusive, high-feedback culture where engineers feel safe to experiment, fail fast, and share learnings across guilds and communities of practice.
• • Serve as the public face of Jiostar data engineering: represent the company at conferences, publish technical blogs, and attract top-tier talent through authentic storytelling about scale, impact, and innovation.