WHY CLARIUM?
The healthcare industry overspends on its supply chain by over $25B each year, the result of fragmented data, inefficient workflows, and wasted supplies. Clarium is fixing that. Our AI-powered platform, Astra OS, gives hospitals end-to-end visibility into their supply chain operations, automating workflows and surfacing actionable insights so supply chain teams can focus on what matters most: patient care. We're trusted by some of the world's leading health systems, including Yale New Haven Health, Stanford, Geisinger, and Kaiser Permanente.
Founded in 2020, Clarium has raised $43M in total funding. Our Series A was led by Northzone, with participation from General Catalyst, AlleyCorp, Kaiser Permanente Ventures, Texas Medical Center Ventures, and 1984 Ventures.
THE OPPORTUNITY
This is a staff-level individual contributor role for an engineer who sets technical direction for our data platform rather than working ticket to ticket. You'll decide how data gets modeled, moved, and trusted across Clarium, and you'll leave both the systems and the engineers around you measurably better.
You'll work across our transactional databases and analytical warehouse, own the pipelines that connect them, and partner directly with the analytics, product, and engineering teams who depend on that data. Much of the work is supply chain data arriving from hospital ERP systems, where schemas and semantics vary by source and are rarely well documented, so a large part of the job is deciding what should be built, not only building it.
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 the observability to back them up
- 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 honest technical feedback
- Partner with analytics, product, and engineering teams to turn 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 why something is slow
- 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; we care more about depth and judgment than an exact stack match
- Production experience running data workloads on AWS (e.g., S3, RDS, Lambda, ECS), with an understanding of the cost, security, and networking implications of how you build
- A track record of leading multi-quarter, cross-team initiatives that depended on teams you don't manage, and comfort with the ambiguity of 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…