Senior Data Engineer

super.money; Bengaluru; On-site; INR 3,600,000 - 7,000,000; Full time

Job summary

super.money in Bengaluru, India seeks a senior data engineer to own data movement, storage, and usage across the organization. You will build data lakes/lakehouses from the ground up, select storage formats, and turn multiple sources into a single governed source of truth with ClickHouse at scale.

You will design ingestion pipelines, ensure data quality and governance, and collaborate with analytics, PMs, and engineers to deliver robust data systems that power insights across teams.

Qualifications

• 7-12 years in data engineering with senior ownership of production data systems.
• Deep ClickHouse knowledge, including engine selection and performance debugging.
• Strong ingestion background with Kafka, CDC, streaming and batch ETL.
• Understanding of data as a product and data governance concepts.
• Experience with Iceberg/Delta/Hudi, object storage and partitioning.

Responsibilities

• Design and operate high-throughput ingestion pipelines (batch + streaming) from diverse sources.
• Own ClickHouse at scale: schema design, partitioning, sharding, materialized views, query optimization, and cluster tuning.
• Build and evolve core data infrastructure for storage, transformation, and serving layers used across teams.
• Design and build the data lake / lakehouse from the ground up: storage architecture, zoning, open table formats, and 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.

Skills

ClickHouse

Streaming Ingestion

Kafka

ETL design

Data Lake / Lakehouse

Data as a Product

SQL / Data modeling

Partitioning / Sharding

Tools

Iceberg

Delta Lake

Hudi

Spark

Trino/Presto

Airflow

Dagster

Job description

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