Data Platform Team Lead

We are building the next generation of digital heart-health products and our data platform is the foundation.
Were looking for a Data Platform Team Lead to own and evolve an AI-first, cloud-native data platform that already:
Serves 100+ data users across the company
Powers ML models impacting thousands of users every day
Supports production systems in a company that literally saves lives
You will lead a growing team of 4 data engineers and 1 BI developer, and work in close, day-to-day partnership with Product, Analytics, Data Science & Engineering
Our future is serving millions of users across multiple products in the heart-health ecosystem. This role owns the platform that will scale us there. We are intentionally building an AI-first / agentic data platform.
That means:
Automating table creation, validation, and testing
Agent-driven monitoring for data quality, freshness, and failures
Using agents to generate and maintain documentation
Reducing manual operational overhead so humans focus on architecture, leverage, and product impact
You will have full organizational support to rethink how a modern data platform should work in an AI-native environment, not incremental improvements, but fundamental design decisions.
Our Technologies stack: Python, Spark, Airflow, DBT, Kafka, AWS (Glue, EMR, S3, Athena and more), Snowflake, Docker, Kubernetes, MongoDB, Redis, Postgres, Elasticsearch, and evolving
Responsibilities:
Platform Leadership & Team Management: Lead, mentor, and grow a team of senior data engineers and BI developers. Set a high bar for technical quality, ownership, and delivery.
Core Data Platform Architecture: Own the design, evolution, and reliability of our cloud-native data platform, balancing scalability, cost, security, and developer velocity.
Deep Collaboration with R&D: Work closely with product, engineering, and ML teams to ensure the data platform enables fast experimentation, production ML, and new product development.
Production-Grade Data Systems: Oversee end-to-end data pipelines, streaming and batch processing, semantic layers, and analytics foundations that serve the entire organization.
Operational Excellence: Ensure data quality, freshness, observability, and incident response meet the standards of a mission-critical system.