Staff Data Platform Engineer - CertifyOS

About CertifyOS

CertifyOS is building the data infrastructure that powers modern healthcare. Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We are backed by leading investors and are building a high-ownership team focused on solving real infrastructure problems that impact millions of patients.

About the Role

We're hiring a Staff Data Platform Engineer to own the data platform that makes CertifyOS the source of truth for provider data. You'll set the architecture, lead a team of engineers to deliver it, and be accountable for the reliability, freshness, and cost of our data.

What You’ll Do

• Lead a team of engineers to deliver data platform initiatives end to end.
• Own data architecture across OLTP, OLAP, and NoSQL stores (Aurora PostgreSQL, Redshift, MongoDB).
• Design and operate distributed real-time and batch pipelines in Python and PySpark (Kafka, Kinesis, SNS/SQS).
• Lead entity resolution and MDM architecture for provider data.
• Build and own data products with clear contracts, SLAs, and consumers.
• Be accountable for data reliability, freshness, quality, and cost, with the observability to prove it.
• Drive performance and scale work: profile, benchmark, and re-architect as volume grows.
• Build the data foundations for AI/ML workloads.
• Mentor engineers, raise the bar on design and code review, and partner with product, analytics, and ML.
• Join the on-call rotation and turn incidents into systemic fixes.

What We’re Looking For

• 10+ years building production data systems, including Staff or Tech Lead scope.
• Led a team of engineers to deliver complex data systems.
• Expert Python and production PySpark.
• Deep distributed systems knowledge: consistency, partitioning, idempotency, backpressure, failure handling.
• Significant entity resolution and MDM experience: matching, deduplication, survivorship, golden records at scale.
• Expertise across OLTP (Aurora, PostgreSQL, MySQL), OLAP (Redshift, Athena, Snowflake, ClickHouse), and NoSQL (MongoDB, DynamoDB, Cassandra).
• Stream processing and event-driven architecture (Kafka, Kinesis, SNS/SQS).
• Data observability in practice: quality checks, lineage, monitoring, alerting.
• Experience handling sensitive data (PII/PHI).
• Fluent with AI-assisted development (Cursor, Claude Code, MCP).

Technologies & Tools

• OLTP: Aurora, RDS PostgreSQL
• OLAP: Redshift, Athena
• NoSQL: MongoDB Atlas
• Streaming: Kafka (MSK), Kinesis, SNS/SQS
• Languages: Python, PySpark
• Infrastructure: AWS, Docker, Kubernetes (EKS), GitHub Actions
• Observability: Sentry, data observability tooling
• AI Tools: Cursor, Claude Code, Codex

Benefits

• 100% coverage of health, dental, and vision insurance premiums.
• Statutory leave benefits and additional wellness (menstrual) leave for women in India.