Staff Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Engineer based in United States.

As a Staff Data Engineer, you will set technical direction for a core data platform rather than simply working through individual tickets.
You’ll shape how data is modeled, ingested, transformed, stored, and served across a growing healthcare technology environment.
Your work will span transactional databases, analytical warehouses, and the pipelines connecting them, with a strong focus on reliability, scalability, and data quality.
You’ll work with complex supply chain data from hospital ERP systems, where schemas and semantics can vary significantly between sources.
The role combines hands-on engineering with architectural ownership, technical mentorship, governance, and cross-functional leadership.
You’ll partner with analytics, product, and engineering teams to turn ambiguous business questions into durable data solutions.
This is a high-impact staff-level opportunity where your technical decisions will influence both the data platform and the engineers working around you.

Accountabilities:
- Own data architecture: Define the architecture and technical roadmap for core data infrastructure across ingestion, transformation, storage, and serving layers on AWS.

- Build reliable pipelines: Design, build, and operate robust batch and near-real-time data pipelines with clear service-level expectations and strong observability.

- Develop data models: Transform supply chain data from external ERP systems into coherent, reusable warehouse models while leading the migration of legacy data assets toward improved structures.

- Optimize performance and cost: Improve performance and efficiency across Postgres and Snowflake through query planning, indexing, partitioning, warehouse sizing, and storage strategy.

- Establish engineering standards: Set expectations for testing, code review, CI/CD, data quality checks, documentation, and other engineering practices across data initiatives.

- Mentor engineers: Provide technical guidance through design reviews, pairing, feedback, and mentorship while helping engineers grow their technical judgment and effectiveness.

- Partner cross-functionally: Work with analytics, product, and engineering teams to translate ambiguous questions into scalable data models rather than one-off extracts.

- Lead data governance: Establish and maintain practices covering data lineage, access controls, retention, auditability, and de-identification where required.

- Drive strategic initiatives: Lead multi-quarter, cross-functional technical initiatives that require influence and coordination across teams you do not directly manage.

Requirements

- Experience: 8+ years of data or software engineering experience, including significant experience owning production systems end to end.

- SQL expertise: Expert-level SQL skills, including the ability to write complex analytical queries, interpret query plans, and identify performance bottlenecks.

- Python: Strong production-level Python experience for pipeline development, transformation logic, testing, and engineering tooling.

- Database expertise: Deep understanding of relational databases, including schema design, normalization tradeoffs, transactions, and performance optimization. Strong Postgres and Snowflake experience is preferred.

- Data engineering tooling: Hands-on experience with pipeline and orchestration technologies such as Airflow, Dagster, Prefect, dbt, Fivetran, Spark, or Kafka, with an emphasis on depth and technical judgment rather than a specific toolset.

- AWS experience: Production experience running data workloads on AWS, including services such as S3, RDS, Lambda, and ECS, with an understanding of cost, security, and networking considerations.

- Technical leadership: Proven ability to lead complex, multi-quarter initiatives across teams without direct management authority and make sound decisions in ambiguous environments.

- Healthcare data: Experience with healthcare data such as claims, EHR/EMR, HL7, FHIR, ICD-10, or CPT is a plus, as is experience working with HIPAA, PHI, de-identification, and audit requirements.

- ERP and supply chain data: Familiarity with supply chain data from ERP systems such as Oracle, Workday, or Lawson is valued, particularly experience handling inconsistent schemas and unreliable source data.

- Infrastructure and automation: Experience with infrastructure-as-code, deployment automation, Terraform, CDK, or CI/CD for data infrastructure is a plus.

- Streaming and observability: Familiarity with streaming or event-driven architectures and data quality or observability tooling is also valued.

- Core technologies: Strong working knowledge of SQL, Python, Postgres, Snowflake, and AWS services including S3, RDS, Lambda, and ECS.

- Additional tools: Familiarity with Terraform, GitHub Actions, EKS/Kubernetes, dbt, Prefect, and Apache Kafka is beneficial.

Benefits

- Compensation: Target base salary range of $170,000–$210,000 USD.

- Equity: Incentive stock options proportionate to salary.

- Healthcare: Top-tier health, vision, and dental benefits.

- Retirement: 401(k) plan.

- Paid time off: Unlimited PTO.

- Work model: Fully remote work, with a NYC coworking space available and opportunities for in-person collaboration across a distributed team.

- Work environment: Opportunity to shape the product and technical foundation at a high-growth stage where individual contributions have meaningful impact.

- Mission: Work on technology designed to reduce administrative friction for frontline healthcare workers and address critical healthcare challenges.

- Collaboration: Work alongside clinicians, engineers, and revenue leaders in a high-caliber, collaborative environment.

- Compensation flexibility: Final compensation and benefits may vary based on the scope of the position and the candidate's knowledge, skills, and experience.