Staff Data Platform Engineer
Summary
Staff engineer who owns CertifyOS's healthcare provider-data platform end to end: setting architecture, leading a team, and being accountable for data reliability, freshness, and cost. Day to day spans Python/PySpark pipelines (Kafka, Kinesis, SNS/SQS), Aurora PostgreSQL/Redshift/MongoDB, entity resolution/MDM, and data foundations for AI/ML workloads, plus on-call.
About CertifyOS
CertifyOS is building the data infrastructure that powers modern healthcare.
Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem.
Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale.
Our vision is simple: One API. One provider ID. Frictionless provider data.
We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re 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
Built and owned data products used by other teams
Shipped measurable performance and scale improvements
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
Built data platforms that serve ML workloads
Influences technical direction across teams and communicates clearly with non-technical stakeholders
Experience handling sensitive data (PII/PHI)
Fluent with AI-assisted development (Cursor, Claude Code, MCP)
Nice to Have
Change Data Capture (CDC) patterns
Data mesh or data-platform-as-a-product approaches
Schema registries or data catalogs
Orchestration tools (AWS Glue, EMR, Managed Flink, Airflow/MWAA, dbt)
Healthcare or other regulated environments
Technologies & Tools
OLTP: Aurora, RDS PostgreSQL
OLAP: Redshift, Athena
NoSQL: MongoDB Atlas
Streaming: Kafka (MSK), Kinesis, SNS/SQS
Python and PySpark
AWS, Docker, Kubernetes (EKS)
GitHub Actions
Sentry and data observability tooling
Cursor, Claude Code, Codex
Benefits of Working at Certify
At Certify, we’re building with intention and taking care of the people doing the work.
Your well-being matters to us. We provide 100% coverage of health, dental, and vision insurance premiums for employees. Our US-based team benefits from unlimited PTO, with at least two weeks off each year to recharge. In India, employees are supported with health insurance, statutory leave benefits, and additional wellness (menstrual) leave for women.
We are an equal opportunity employer committed to building an inclusive environment where everyone feels valued and empowered to do their best work, and we welcome applicants from all backgrounds and experiences.
If you require reasonable accommodations during the application process, please contact recruiting@certifyos.com.
We are also committed to pay transparency and foster an open culture where compensation conversations are encouraged and respected.
Skills
- AI
- Airflow
- Analytics
- API
- Athena
- Aurora
- AWS
- Aws Glue
- Cassandra
- Claude Code
- ClickHouse
- Data Mesh
- dbt
- Distributed Systems
- Docker
- DynamoDB
- EKS
- Event Driven Architecture
- Flink
- GitHub
- GitHub Actions
- Kafka
- Kinesis
- Kubernetes
- Machine Learning
- MCP
- MDM
- MongoDB
- MySQL
- NoSQL
- Observability
- OLAP
- PostgreSQL
- PySpark
- Python
- RDS
- Redshift
- Sentry
- Snowflake
- SNS
- SQS
As published by ashby · 11 questions
Basics
First and Last Name, Email, Location, Resume
Short answers (5)
- Phone
- LinkedIn Profile optional
- What is your notice period or earliest available start date? optional
- What is your current compensation? optional
- What are your compensation expectations for this role?
Pick from a list (6)
- Are you legally authorized to work in the country where this role is based?
- How many years of professional software engineering experience do you have in a Data Platform or Data Engineering role?
- What is your proficiency level in designing and managing distributed data processing systems using Spark, Flink, or Ray?
- Which programming language do you use fluently to build data platform services and infrastructure?
- Do you have hands-on experience architectural ownership of cloud-native data warehouses/lakes (e.g., Snowflake, BigQuery, Databricks, Iceberg)?
- What is your experience level with data orchestration tools (e.g., Airflow, Dagster, Prefect) and infrastructure-as-code (e.g., Terraform)?



