Staff Software Engineer, Data

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Software Engineer, Data based in United States.** This is a senior technical leadership role focused on shaping and scaling a modern data engineering platform that powers critical analytics and product capabilities. You’ll own some of the team’s most complex and ambiguous technical challenges, turning them into clear strategies, designs, and production-ready solutions. Working across engineering, product, infrastructure, and leadership, you’ll influence architecture and the platform’s multi-quarter technical direction. You’ll combine hands-on engineering depth with staff-level mentorship, design leadership, and pragmatic technical judgment. The role offers the opportunity to improve reliability, scalability, developer productivity, and data quality across a distributed engineering organization. You’ll also help define responsible, effective practices for AI-assisted software development as these tools continue to evolve. This is a fully remote position with meaningful impact on technology supporting complex healthcare operations and vulnerable patient populations. ### Accountabilities - Drive the technical direction of the ELT and data platform over a multi-quarter horizon, balancing strategic investments, technical debt, reliability, and pragmatic delivery. - Translate business and product goals into technical strategies, architectural designs, and shippable initiatives that can be executed effectively by multiple engineers. - Author and maintain high-quality design documents that establish technical direction and serve as canonical references for the engineering organization. - Provide architectural guidance across the codebase and make high-impact tradeoff decisions spanning data, application boundaries, infrastructure, deployment, observability, and reliability. - Identify systemic risks, scalability constraints, security concerns, data-quality issues, and potential failure modes early, incorporating mitigation into the technical roadmap. - Provide deep, substantive reviews of pull requests and design proposals, raising engineering standards beyond individual implementation details. - Mentor and coach senior engineers while helping establish growth paths and technical expectations across the team. - Set and maintain standards for code quality, testing, observability, security, reliability, and HIPAA-compliant handling of protected health information. - Lead technical response to the most complex production incidents and ensure follow-up improvements prevent recurring failures. - Partner with product, leadership, and cross-functional engineering teams to align platform investments with measurable business outcomes. - Use AI-assisted development tools, including Claude Code, to increase engineering effectiveness and help establish thoughtful team-wide practices around AI-enabled workflows. - Continuously improve the platform so engineers can work with greater speed, confidence, reliability, and independence. ## Requirements - 7+ years of professional software engineering or data engineering experience, demonstrating progressively broader technical leadership. - Deep expertise in SQL and Python, with a strong software-engineering approach to data engineering. - Extensive hands-on experience with Airflow or a comparable orchestration platform, dbt or another modern transformation framework, and a cloud data warehouse such as Snowflake, BigQuery, or Redshift. - Proven experience solving scaling, performance, reliability, and infrastructure challenges at the warehouse and data-platform levels. - Strong expertise in at least one adjacent area such as data platform architecture, HIPAA-compliant data handling, or reliability engineering. - Demonstrated ability to anticipate architectural flaws, edge cases, scaling constraints, and organizational risks before they materially affect delivery. - Experience owning technical roadmaps, converting ambiguous requirements into actionable designs, and guiding multiple engineers through delivery. - Proven track record of mentoring engineers and influencing technical direction beyond individual projects. - Comfort working across application boundaries, infrastructure, deployment tooling, observability, and incident response. - Strong judgment regarding when and how to use AI-assisted development, along with the ability to establish effective engineering practices around these tools. - Quality-driven mindset with a strong emphasis on testing rigor, maintainability, reliability, and operational excellence. - Excellent written and verbal communication skills, particularly the ability to align engineering, product, and leadership through clear technical documentation. - Pragmatic approach to engineering decisions, knowing when to make long-term investments, reduce technical debt, or ship an imperfect but valuable solution. - Experience with healthcare data standards such as HL7 or FHIR, EHR integrations, value-based care, PACE, Medicare/Medicaid populations, Kubernetes, Snowflake, MongoDB, or observability tooling is a plus. - Experience in staff/principal-level data platform roles, technical hiring or organizational design, or machine learning/AI applications in healthcare analytics is also advantageous. - Must be based in the United States; this position is not eligible for sponsorship. ## Benefits - **Competitive base salary:** $180,000–$190,000 annually, with final compensation based on experience, skills, and organizational needs. - **Fully remote:** Work remotely from anywhere in the United States. - Opportunity to shape the technical direction of a mission-driven healthcare data platform. - Significant staff-level ownership over architecture, engineering standards, reliability, and platform evolution. - Collaborative, remote-first environment built around ownership, data-informed decision-making, innovation, impact, and mutual support. - Opportunity to mentor engineers and influence engineering practices across the broader organization. - Exposure to modern technologies including Snowflake, Airflow, Airbyte, dbt, Kubernetes, Python, SQL, Grafana, Prometheus, Loki, and AI-assisted development tools. - Meaningful opportunity to improve technology supporting care delivery and outcomes for complex and socially vulnerable populations.