Senior Data Engineer

super.money; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

super.money is seeking a Senior Data Engineer to own data movement, storage, and usage across the organization. You will build data lakes/lakehouses from scratch, design storage architectures, and ensure a unified source of truth.

Deep ClickHouse expertise is essential to optimize, scale, and operate high-throughput pipelines. You will collaborate with engineering, analytics, and product teams to translate business needs into robust data systems, while treating data as a product with governance

Qualifications

• 7-12 years of data engineering experience in production systems.
• Deep ClickHouse knowledge including engine selection, TTLs, projections, replication, and performance tuning.
• Strong ingestion background with Kafka/CDC/streaming and batch ETL.
• Experience designing data lakes/lakehouses with open formats (Iceberg/Delta/Hudi) and storing data as a product.
• Familiarity with data governance, lineage, and observability.
• Proficiency with orchestration tools (Airflow/Dagster or equivalent).

Responsibilities

• Design and operate high-throughput ingestion pipelines (batch + streaming).
• Own ClickHouse at scale: schema, partitioning, sharding, materialized views, and cluster tuning.
• Build and evolve data infrastructure across storage, transformation, orchestration, and serving layers.
• Architect data lake/lakehouse from ground up with raw → refined → serving zones.
• Treat data as a product with SLAs, contracts, lineage, and self-serve access.
• Model data end-to-end from source to consumption layers.
• Own data quality, governance, and correctness across the flow.
• Collaborate with engineering, analytics, PMs, and stakeholders to translate needs into robust systems.
• Mentor engineers and set technical direction for the data org.

Skills

Data engineering experience

ClickHouse expertise

Streaming + batch ETL

Data as a product mindset

Orchestration knowledge

Tools

Kafka

Airflow

Dagster

Spark

Trino

Presto

Iceberg

Delta

Hudi

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