Director, Data and Platform

Position Summary

We're adding a director of data and platform architecture to a small, high-leverage AI engineering team. You'll own the full data architecture end to end: how data lands in our systems, how it moves through ETL and transformation, and how it ultimately reaches stakeholders through extracts and dashboards. You'll also own devops and infrastructure for the AI engineering team, meaning you decide how everything gets built, deployed, and kept running. You'll set that architecture and build it yourself. This is a hands-on keyboard role, even at the director level.

This role comes with direct reports from the start. You'll build and lead the team as it grows, while staying close enough to the work to keep shipping code yourself.

Essential Duties and Responsibilities

• Own the data architecture end to end, from the moment data lands in our systems through ETL and transformation, extracts, and the dashboards stakeholders rely on.
• Own devops and infrastructure for the AI engineering team end to end. Design and own the platform architecture underlying our AI engineering systems, from ingestion through serving.
• Set technical direction and standards for data infrastructure, pipelines, ETL and transformation logic, and platform reliability.
• Design the extract and reporting layer so downstream dashboards and analytics are accurate, timely, and trusted.
• Build the systems and infrastructure you design, and make them production grade: deployable, observable, and stable enough to run without hand holding.
• Write and ship code yourself.
• Build and lead the data and platform team as it grows, from hiring through day-to-day direction.
• Mentor engineers on architecture, data design, devops, and/or platform thinking.
• Communicate tradeoffs clearly to engineers, other architects, and leadership. Turn complex systems into decisions people can act on.
• Partner with AI engineering leadership to align platform investments with product and research priorities.
• Translate business and executive priorities into data strategy and architecture decisions, so technical investments map directly to what the business needs next. You own the outcome of those decisions.

Education/Experience/Competencies

• 8+ years architecting and building platforms in production. Not a hard cutoff: strong candidates with less experience can still be considered.
• Proven, hands-on experience owning data architecture end to end, including ingestion, ETL and transformation, extracts, and the reporting or BI layer.
• Proven, hands-on devops experience: you build, deploy, and operate the systems and infrastructure you design, and what you ship performs in production at scale.
• A track record of writing and shipping production code yourself, not just producing diagrams and design documents.
• Hands-on depth across the modern data stack: big data/distributed processing (e.g., Spark, Kafka), NoSQL and document stores (e.g., MongoDB, DynamoDB), data warehousing (e.g., Snowflake, Redshift, BigQuery), and high-throughput transactional databases (Postgres, MySQL) with rigorous PHI/PII handling and detection practices.
• Hands-on experience with devops tooling and practices: GitOps workflows, Kubernetes, and cloud infrastructure (AWS or equivalent).
• Excellent communication and collaboration skills. You translate complex architecture decisions into terms engineers, other architects, and non-technical leadership can act on, and you do it consistently, not just in big reviews. You work across teams by default, aligning platform and data decisions with product, research, and business priorities rather than building in isolation.
• Experience mentoring engineers on architecture, data design, devops, and/or platform thinking.
• Prior experience building and leading a team, or clear readiness to take on direct reports.
• Comfort working in a small, fast-moving team where you'll wear multiple hats.

Bonus Qualifications

• Experience architecting data platforms that support AI or ML workloads.
• Experience with vector databases for AI/ML retrieval (e.g., Qdrant, Pinecone, Weaviate).
• Experience in healthcare, pharmacy benefits, or another regulated data environment.
• Experience standing up data platforms from scratch (greenfield), not just extending or migrating existing systems.

Salary Range: 220,000 - 260,000