Senior Data Engineer

Senior Data Engineer About Leap Leap is one of the fastest-growing benefits solutions and a category-defining pioneer in employer specialty pharmacy. We are reshaping how life-changing therapies are delivered and financed, ensuring patients get the treatment they need while employers finally get a fair deal. Specialty drugs and infusions represent nearly 10% of all healthcare spend and are the fastest-growing cost category for employers. Leap tackles this challenge with a novel approach: eliminating hidden markups, expanding access to high-quality infusion providers, and bringing clarity and fairness to how therapies are priced and paid for. We’re proud to partner with numerous Fortune 500 companies and leading TPAs. Each patient we serve creates immediate ROI: lower costs, improved access, and better care. Join us as we redefine what’s possible in specialty care. ABOUT THE ROLE You'd be the most senior data person on the team. You'll own the pipelines, the warehouse, and the reporting layer, and you'll make the design decisions about how they're built. You'll report to the engineering lead and work directly with clinical ops, business operations, and leadership. Small engineering team, high ownership. KEY RESPONSIBILITIES Pipelines and Warehouse - Build and own data pipelines and ETL for claims ingestion, drug pricing, and CRM sync (BigQuery, Python) - Design production pipelines for batch and streaming workloads — claims data is high-volume today, and new large-scale data sources are coming - Design warehouse schemas and transforms with clear separation between raw, staging, and modeled layers - Maintain data quality and reliability across systems that feed both human users and AI workloads — this means row-count checks, schema drift detection, anomaly alerting, and knowing when upstream sources have silently changed, not just whether the job ran Data Governance - Build pipeline monitoring that tells you whether the data is right, not just whether the job ran - Design for recoverability. Pipelines should be idempotent and replayable, with raw data always preserved so you can reprocess when logic changes - Track data lineage: where it comes from, how it's transformed, and what depends on it - Validate data at every stage before it reaches a dashboard or an AI system Reporting Infrastructure - Build reporting systems that give sales, clinical, and leadership teams live visibility into the business - Create automated alerting that surfaces when something has changed, so the team acts on data instead of asking for it AI-Ready Data Infrastructure - Build PHI-safe pipelines that feed LLM workloads, agent systems, and automation - Design data architecture that connects claims, drug pricing, patient records, CRM activity, and clinical workflows into a usable whole - Own the ingestion of external data from non-standard formats and sources — we work with many providers who each send data differently, and new sources are added regularly QUALIFICATIONS Required - Python, SQL, and dbt. You've worked with BigQuery, Snowflake, or a similar cloud warehouse and know your way around orchestration tools (Airflow, Dagster, Prefect, or similar). - You've architected data platforms, not just written pipelines. You've made decisions about batch vs streaming, incremental vs full-refresh, and warehouse structure — and you can explain why. - You care about monitoring, lineage, and governance. You've built systems where you can trace data from source to report. - You use AI tools in your own work and you know how to build data infrastructure that AI systems can rely on in production. - You've been an early employee, a solo data person, or the one who built the data stack from scratch. Preferred - Healthcare or HIPAA experience, Fivetran or similar ingestion tools, CRM integrations (Salesforce, HubSpot), or experience building data infrastructure for LLM/AI workloads - Experience with streaming frameworks (Kafka, Pub/S ... (truncated, view full listing at source)