Senior Data Engineer

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.

Responsibilities:

• 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 are 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.

Requirements:

• 7+ years in data engineering, with senior ownership of production data systems.
• 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 and 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.