Senior Data Infrastructure Engineer

The healthcare industry overspends on its supply chain by over $25B each year due to fragmented data, inefficient workflows, and wasted supplies. Clarium is addressing this issue with its AI-powered platform, Astra OS, which provides hospitals with end-to-end visibility into their supply chain operations. The platform automates workflows and surfaces actionable insights, enabling supply chain teams to focus on patient care. Clarium is trusted by leading health systems such as Yale New Haven Health, Stanford, Geisinger, and Kaiser Permanente.

Founded in 2020, Clarium has secured $43M in total funding, with its Series A round led by Northzone and participation from General Catalyst, AlleyCorp, Kaiser Permanente Ventures, Texas Medical Center Ventures, and 1984 Ventures.

In This Role You Will

• Own the architecture and technical roadmap for core data infrastructure on AWS, spanning ingestion, transformation, storage, and serving layers.

• Design, build, and operate reliable batch and near-real-time pipelines with clear SLAs and robust observability.

• Model supply chain data from external ERP systems into a coherent, reusable warehouse model, and lead the migration of legacy assets toward it.

• Tune performance and cost across Postgres and Snowflake, including query plans, indexing, partitioning, warehouse sizing, and storage strategy.

• Establish engineering standards for data work (testing, code review, CI/CD, data quality checks, documentation) and mentor engineers through design reviews, pairing, and technical feedback.

• Partner with analytics, product, and engineering teams to transform ambiguous questions into durable data models instead of ad hoc extracts.

• Lead governance for sensitive data, including lineage, access controls, retention, auditability, and de-identification where required.

What You'll Bring

• 8+ years of data or software engineering experience, including significant time owning systems end-to-end in production.

• Expert-level SQL: you write complex analytical queries as a matter of course and can read a query plan and explain performance bottlenecks.

• Strong Python for production data work, including pipeline code, transformation logic, testing, and tooling.

• Deep experience with relational databases, including schema design, normalization tradeoffs, transactions, and performance tuning (Postgres and Snowflake strongly preferred).

• Hands-on experience with data pipeline and orchestration tooling such as Airflow, Dagster, Prefect, dbt, Fivetran, Spark, or Kafka.

• Production experience running data workloads on AWS (e.g., S3, RDS, Lambda, ECS), with an understanding of cost, security, and networking implications.

• A track record of leading multi-quarter, cross-team initiatives that depended on teams you don’t manage, and comfort with ambiguity in deciding what should be built.

Nice to Have

• Experience with healthcare data (claims, EHR/EMR, HL7 or FHIR, ICD-10, CPT) or working under HIPAA with PHI, de-identification, and audit requirements.

• Familiarity with supply chain data from ERP systems such as Oracle, Workday, or Lawson, including where it tends to be unreliable.

• Infrastructure-as-code and deployment automation (Terraform, CDK, CI/CD for data infrastructure).

• Streaming and event-driven architectures, or data quality and observability tooling.

Skills & Tools You'll Use

Need to Know: SQL, Python, Postgres, Snowflake, AWS (S3, RDS, Lambda, ECS)

Nice to Know: Terraform, Github Actions, EKS / Kubernetes, dbt, Prefect, Apache Kafka