5+ years of software engineering experience, particularly in building or operating data platforms at scale.
Proficient with modern data stack components: CDC (Debezium), streaming (Kafka), data lake formats (Iceberg, Delta, Hudi), and MPP/lakehouse engines (StarRocks, Snowflake).
Strong SQL skills with expertise in schema design and query optimization for large datasets.
Experience managing production data infrastructure, including orchestration and data quality.
Familiarity with HIPAA regulations and handling of PHI/PII data.
Responsibilities
Own the end-to-end data warehouse platform, including CDC pipelines and analytics tooling.
Design and operate CDC pipelines using Debezium and a streaming backbone for efficient data movement.
Architect the data lake on object storage with open table formats for batch and streaming workloads.
Run and scale StarRocks as the query and serving layer, focusing on schema design and performance tuning.
Build the transformation layer with dbt, ensuring modeling standards and documentation are in place.
Implement orchestration and CI/CD processes, along with observability and data-quality tools.
Collaborate with Security and Compliance to ensure the platform meets HIPAA and SOC 2 standards.
Benefits
Opportunity to work with cutting-edge technologies in a hands-on role.
Broad scope of responsibilities, allowing for significant impact on the data platform.
Collaborative environment with cross-functional teams, enhancing professional growth.
Focus on building a trustworthy data platform with strong governance and compliance standards.
Full Job Description
About the Role
We're hiring a Senior Software Engineer to own Commure's data warehouse platform end-to-end. You'll design, build, and operate every layer of the stack:
CDC pipelines
Streaming transport
Schema governance and data contracts
Query and serving layer
Analytics platform
The core stack today: Debezium for CDC, StarRocks as our MPP query and serving engine, and dbt for transformation and modeling. This is a hands-on IC role with broad scope. You'll make architectural calls, write the code that matters most, and establish the patterns and conventions on the data platform.
What You'll Do
Own the data warehouse platform end-to-end: CDC pipelines, data lake, query layer, transformation layer, and the analytics-facing tooling that sits on top.
Design and operate CDC pipelines with Debezium (and Kafka, Redpanda, or an equivalent streaming backbone) that move data from operational databases into the warehouse with low latency and high fidelity.
Architect the data lake on object storage using an open table format (Iceberg, Delta Lake, or Hudi) with Parquet, enabling both batch and streaming workloads and clean separation of storage from compute.
Run and scale StarRocks (or adjacent MPP/lakehouse engines) as the query and serving layer - schema design, materialized views, ingestion patterns, tuning, and cost/performance trade-offs.
Build the transformation layer with dbt: modeling standards, tests, documentation, and a semantic layer that gives every team a single source of truth for metrics.
Stand up orchestration (Airflow, Dagster, or similar) and the CI/CD, observability, and data-quality tooling that make the platform trustworthy day-to-day.
Partner with Security and Compliance on PHI/PII handling, access controls, lineage, and auditability so the platform meets HIPAA and SOC 2 bar by default.
Collaborate with product and analytics teams on schema contracts, ingestion patterns, and self-serve tooling so they can build on the platform with confidence.
What You Have
5+ years of software engineering experience, with significant time building or operating data platforms at scale.
Experience across the modern data stack: CDC (Debezium or equivalent), streaming (Kafka, Redpanda), data lake formats (Iceberg, Delta, Hudi), an MPP or lakehouse query engine (StarRocks, ClickHouse, Trino, Snowflake, Databricks), and dbt.
Fluent in SQL, schema design, query optimization, and reasoning about cost and latency trade-offs on large datasets.
Experience running production data infrastructure (orchestration, observability, on-call, data quality, and incident response).
Preferred
Direct experience with Debezium, StarRocks, and dbt in production.
Experience building semantic layers (dbt Semantic Layer, Cube) or data catalogs / lineage (DataHub, OpenMetadata, Amundsen).
Experience with HIPAA-regulated data (PHI handling, de-identification, and access governance).
