Sarvam - Staff Data Engineer

• 5+ years of backend / data-platform engineering, with a meaningful chunk spent designing and operating analytics systems in production. • Track record of building 0-to-1 platforms that other engineering teams depend on. • Must-haves • ClickHouse in production at scale — sharding, replication, MergeTree variants, materialized views, projection design, query tuning, and operational know-how (backups, upgrades, capacity planning). • ClickHouse • Golang for high-throughput backend services — strong grasp of concurrency, performance, and API design (REST/gRPC). • Golang • Kafka as a primary streaming backbone — partitioning strategy, consumer groups, exactly-once / idempotency patterns, schema registry. • Kafka • Debezium-based CDC — you've built and operated CDC pipelines from transactional databases into analytical stores. • Debezium-based CDC • Strong systems instincts: you reason about throughput, latency, cost, and failure modes before you write code. • Nice-to-haves • Spark + Airflow for batch enrichment, backfills, and scheduled aggregations. • Spark + Airflow • Experience building multi-tenant SaaS platforms with row-level security, per-tenant quotas, and tenant-aware RBAC. • multi-tenant SaaS • Worked on or integrated with BI / dashboarding tools (Superset, Metabase, Cube, or similar) — bonus if you've built embedded customer-facing analytics. • BI / dashboarding tools • Exposure to billing / metering data feeds where correctness is non-negotiable. • You'll thrive here if you • Want to be the technical owner of a foundational platform, not a feature contributor on someone else's system. • Like writing code as much as writing design docs — and refuse to do one without the other. • Care about cost-per-query and dollar-per-event, not just p99 latency. • Are comfortable making decisions with imperfect information, and revisiting them when the data changes. • Want to work on-site in Bengaluru with a small team of senior builders, shipping fast.