Data Platform Lead

What You'll Do

• Own the data platform strategy end-to-end — from ingestion architecture and scalable, multi-tenant data infrastructure to transformation pipelines, a modern BI layer, and how the platform grows as we add clients and data sources.
• Drive the AI-on-data layer. Partner with our AI engineer to define how agents and LLMs access, query, and act on platform data — semantic models, retrieval patterns, and in-warehouse AI primitives.
• Build and productize our integrations motion — ingesting data from a growing set of first- and third-party sources. Turn what today requires custom work into a repeatable, operable pattern.
• Lead and develop our data engineering team. You'll manage directly, set technical direction, and raise the bar on quality — starting with a tight-knit team with room to grow.
• Define the engineering standards for the data org — CI/CD, testing, infra-as-code, data lineage, governance, and observability — so the platform scales without fragility.
• Be a strong hands-on presence on the warehouse and transformation layer — fluent enough in Snowflake to contribute meaningfully alongside the team, not just oversee it.
• Partner with go-to-market teams to define what great looks like for client onboarding and data delivery, and drive engineering execution against that bar.

What We're Looking For

• 8+ years in data engineering, with at least 2 years in a leadership role — you have a proven track record of managing and developing engineers
• Strong people management instincts: clear communicator, good at developing talent, comfortable giving direct feedback, and able to build a high-performance culture even with a small team.
• Hands-on experience with a modern cloud data warehouse and transformation stack — Snowflake + dbt strongly preferred; Redshift, Databricks, or BigQuery with a fast ramp is acceptable.
• Proven experience building AI on top of structured data — semantic layers, agent/LLM access patterns to warehouses, or retrieval-augmented generation.
• Deep expertise in data ingestion at scale — you've built or owned the systems that pull from many disparate sources into a warehouse. You know when to use an off-the-shelf connector, when to build, and how to make either one operable at scale.
• Experience building and shipping a productized, multi-tenant data offering — client isolation, onboarding flows, SLAs, and ongoing support. You think in products, not projects.
• Solid engineering fundamentals: version control, code review, CI/CD, infra-as-code — and a bias toward standards that teams can repeat, not heroics that only you can maintain.

Nice to Haves

• Direct Snowflake + dbt experience, and familiarity with advanced in-warehouse AI capabilities and agent-accessible data patterns.
• ML or MLOps experience — feature stores, training pipelines, model evaluation — and a track record of building tools that extract measurable value from data.
• AWS fluency — we run on AWS, and comfort with IAM, S3, Lambda, and Glue is a plus.
• Experience with modern BI tooling and self-serve analytics delivery.
• Background in sports, live events, or time-series data; experience with EU data residency, GDPR, or multi-region warehouse patterns.

Benefits

• Remote first
• Competitive salary and equity
• Flex PTO policy
• 401(k)
• Generous medical, dental and vision plans
• 16 weeks paid parental leave for primary and secondary caregivers
• $1,000 reimbursement for work-from-home tech setup
• Company-paid sustainability subscription to ensure carbon neutrality is maintained for employee activities, such as travel