# Staff Software Engineer, Data Warehouse

**Company:** [Commure](https://hotfix.jobs/companies/commure)
**Location:** Mountain View, CA, Los Angeles, CA, New York, NY
**Role:** Data Engineering
**Salary:** $210k – $275k/yr
**Experience:** 7+ years
**Skills:** Debezium, Kafka, Redpanda, Iceberg, Delta Lake, Hudi, Parquet, Starrocks, dbt, SQL, Airflow, Dagster, Terraform, Kubernetes, HIPAA
**Posted:** 2026-09-04

> Staff Software Engineer responsible for architecting, building, and operating Commure’s data warehouse platform, including CDC, lakehouse, query, transformation, and analytics layers. Requires 6+ years of software engineering experience and broad expertise across modern production data infrastructure.

## Job Description

## Responsibilities
- Own the data warehouse platform end-to-end, including CDC pipelines, data lake, query and serving layers, transformation, and analytics tooling.
- Design and operate low-latency, high-fidelity CDC pipelines using Debezium and Kafka, Redpanda, or an equivalent streaming backbone.
- Architect data lakes on object storage using Iceberg, Delta Lake, or Hudi with Parquet for batch and streaming workloads.
- Run and scale StarRocks or comparable MPP/lakehouse engines, including schema design, materialized views, ingestion, tuning, and cost/performance optimization.
- Build dbt transformation models, tests, documentation, and a semantic layer for consistent metrics.
- Establish orchestration, CI/CD, observability, and data-quality tooling.
- Partner with Security and Compliance on PHI/PII handling, access controls, lineage, and auditability for HIPAA and SOC 2 compliance.
- Define schema contracts, ingestion patterns, and self-service tooling for product and analytics teams.

## Requirements
- 6+ years of software engineering experience, including substantial experience building or operating data platforms at scale.
- Experience with CDC, streaming, data lake formats, MPP or lakehouse query engines, and dbt.
- Fluency in SQL, schema design, query optimization, and cost and latency trade-offs for large datasets.
- Experience operating production data infrastructure, including orchestration, observability, on-call, data quality, and incident response.

## Nice-to-haves
- Production experience with Debezium, StarRocks, and dbt.
- Experience with semantic layers such as dbt Semantic Layer or Cube, and data catalogs or lineage tools such as DataHub, OpenMetadata, or Amundsen.
- Experience with HIPAA-regulated data, including PHI handling, de-identification, and access governance.
- Experience supporting AI/ML workloads, including feature stores, training-set curation, embedding pipelines, or retrieval systems.
- Experience across AWS, Google Cloud, and Azure; infrastructure as code with Terraform or Pulumi; and Kubernetes controllers.

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