Director Data Platform

Salary Range: 200000 to 225000 (Currency: USD) (Pay period: per-year-salary)

You will

ARCHITECT THE DATA FOUNDATION

Take end-to-end ownership of the data lifecycle, managing everything from pipeline orchestration and our dbt project to the warehouse infrastructure, ensuring peak performance and reliability.

Instill a "Data-as-a-Product" philosophy across the organization, treating internal teams as customers by defining rigorous SLAs for data quality, freshness, and accessibility.

Establish proactive data observability and contracts, shifting the culture from reactive troubleshooting to a disciplined system that identifies regressions before they impact downstream stakeholders.

Translate ambiguous business needs into concrete technical roadmaps, leading discovery with cross-functional partners to turn recurring pain points into scalable architectural solutions.

Direct the evolution of our foundational models, applying expert-level dimensional modeling and star schema designs to ensure the platform scales with our AI and analytics footprint.

Serve as a strategic partner to Analytics & Data Science, co-architecting the semantic and context layers to establish the standards they build upon.

Maintain a documented, transparent boundary between our core product systems and the analytics environment to ensure clarity of ownership.

EMPOWER AND SCALE THE TEAM

Mentor and scale a high-performing, distributed data engineering team comprising domestic and offshore talent.

Delegate with strategic intent, focusing on expanding platform capacity through the team's technical growth rather than your own individual keyboard output.

Drive technical excellence and architectural discipline through rigorous design reviews, RFCs, and code audits.

PARTNER STRATEGICALLY ACROSS THE BUSINESS

Act as the technical counterpart to the Analytics & Data Science leadership, ensuring platform investments align with the company's long-term trajectory.

Conduct standing roadmap reviews with key stakeholders to prioritize effectively, unblock initiatives, and ensure capacity allocation is transparent.

Collaborate with cross-functional pods to ensure data requirements are integrated into the problem definition phase, creating durable rather than isolated solutions.

Prioritize fixing classes of problems over instances, building permanent platform capabilities that solve recurring stakeholder challenges.

STEWARD CRITICAL DATA OPERATIONS

Direct the data infrastructure supporting revenue cycle management and billing, ensuring the pipelines powering Finance remain flawless.

Manage the integrations and models for our member lifecycle systems, guaranteeing that engagement and attribution data remain consistent across the warehouse.

Build resilience into vital business reporting through sophisticated automation, comprehensive documentation, and a culture of shared accountability.

Lead the platform's HIPAA compliance efforts, architecting the controls for PHI access, deidentification, and audit readiness in partnership with Security.

LEAD AI-NATIVE ENGINEERING

Foster an AI-first engineering culture where tools like Claude Code serve as genuine force multipliers for refactoring and documenting infrastructure.

Establish the governance standards and safety guardrails required for the responsible application of AI within our engineering workflows.

Design the underlying context layer for our AI-powered analytics tools, ensuring generated insights are accurate and dependable by construction.

You are

• A hands-on technical leader who focuses on scaling through team capability, prioritizing architectural depth over individual keyboard output.
• A systems thinker who prioritizes fixing the class of problem over the instance, building durable solutions that solve recurring challenges.
• A strategic partner to cross-functional pods, capable of translating complex business needs into concrete technical roadmaps for stakeholders.
• A "Bridge-Builder" who moves seamlessly between architecting with senior engineers and explaining the organizational impact of data performance to leadership.
• Pragmatic regarding technical debt, balancing delivery speed with the architectural discipline required to maintain a performant, stable platform.
• Expertly opinionated about dimensional modeling and semantic layers, ensuring the platform footprint scales without creating fragmented definitions.
• Clear-eyed about the steward’s responsibility when architecting controls for PHI and sensitive clinical datasets.

You have

Over a decade of expertise in data engineering or platform domains, with at least 4 years dedicated to leading and scaling high-performing teams.
Demonstrated hands-on mastery of modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and sophisticated orchestration frameworks like Dagster or Airflow.
Expert-level dbt proficiency, encompassing large-scale project architecture, semantic layer implementation, and seamless integration with BI tools such as Looker or Omni.
Advanced command of dimensional modeling at scale, supported by expert SQL abilities and strong Python engineering skills.
A deep commitment to Data Reliability Engineering (DRE), with a proven history of architecting CI/CD pipelines and automated quality testing within dbt ecosystems.
The ability to influence without authority, successfully driving the adoption of architectural standards and new tooling across cross-functional engineering pods.
Experience serving as a strategic technical peer to Analytics and Data Science teams, functioning as an architectural partner rather than a support service.
A successful background in managing distributed engineering talent, including domestic and offshore collaborators.
Professional fluency in HIPAA regulations and experience architecting infrastructure for sensitive healthcare data, including claims, eligibility, and clinical records.
Operational experience with AI-native development tools like Cursor or Claude Code, utilizing them as force multipliers in production workflows.

Signals of Success

• Cross-functional teams in Product and Finance accelerate their velocity because the underlying data foundation is accurate, stable, and self-service.
• Ambiguity regarding metric accuracy is eliminated through a unified context layer that synchronizes definitions across BI, experimentation, and AI platforms.
• Platform priorities and roadmaps are communicated transparently, ensuring every stakeholder understands the strategic rationale behind capacity allocation.
• Vital business reporting and financial pipelines operate with high resilience, free from single points of failure or reactive troubleshooting.
• Your engineering team achieves greater technical depth and autonomy under your mentorship and leadership.