Senior Data Engineer

super.money · India

Data Engineer 4 / Lead Data Engineer

Data Platform & Engineering

ABOUT THE ROLE:

We're looking for a senior data engineer to own how data moves, lives, and is used across the entire org - from ingestion and storage to a unified data store, transformation, serving, and analytics. A core part of this role is deeply understanding how to design and build data lakes / lakehouses from the ground up: choosing storage and table formats, laying out zones, and turning scattered sources into one governed source of truth. You'll treat data as a product across the board - reliable, discoverable, and built for scale. Deep ClickHouse expertise is essential.

WHAT YOU'LL DO:

• Design and operate high-throughput ingestion pipelines (batch + streaming) from diverse sources across the org

• Own ClickHouse at scale: schema design, partitioning, sharding, materialized views, query optimization, and cluster tuning

• Build and evolve core data infrastructure — storage, transformation, orchestration, and serving layers used across teams

• Design and build the data lake / lakehouse from the ground up: storage architecture, zoning (raw → refined → serving), open table formats (Iceberg / Delta / Hudi), and it as the org's single source of truth

• Treat data as a product org-wide: SLAs, data contracts, lineage, observability, and self-serve access for every consumer

• Model data end-to-end: from source capture through transformation to the layers analysts, services, and products actually query

• Own data quality, governance, and correctness across every stage of the flow

• Partner with engineering, analytics, PMs, and other stakeholders to translate business needs into robust data systems

• Mentor engineers and set technical direction for the data org

WHAT YOU'LL BRING:

• 7-12 years in data engineering, with senior ownership of production data systems

• Deep ClickHouse knowledge — not just querying, but operating it: engine selection (MergeTree family), TTLs, projections, replication, and performance debugging

• Strong ingestion background — Kafka / CDC / streaming and batch ETL, handling schema drift, backfills, and late or duplicate data

• Genuine understanding of data in and out: how it's produced upstream, how it moves, and how it's consumed downstream

• Data lake & unified-store experience — designing a data lake / lakehouse as a unified store: open table formats (Iceberg / Delta / Hudi), object storage, partitioning and file layout, and query engines over it (Spark / Trino / Presto)

• Experience treating data as a product across the org — thinking about consumers, reliability, contracts, and interfaces

• Comfort with orchestration (Airflow / Dagster / etc.) and modern data infrastructure

NICE TO HAVE:

• Experience scaling ClickHouse clusters in production

• Streaming ingestion into the lake (Kafka → object storage, CDC, exactly-once)

• Data catalog / governance tooling (e.g. Glue, Unity Catalog, DataHub)

• Real-time analytics / OLAP at high volume

• dbt or similar transformation frameworks