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About LeapLeap 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 RoleThe Senior Data Engineer is responsible for owning Leap's data infrastructure end-to-end — from ingestion pipelines and warehouse architecture to the reporting layer that drives business decisions. This role partners closely with clinical operations, business operations, and leadership to ensure that data is reliable, traceable, and ready to power both human users and AI workloads. You will own the design decisions about how the data stack is built and evolved, operating with high autonomy in a small, fast-moving engineering team.Key ResponsibilitiesPipelines and WarehouseBuild and own data pipelines and ETL processes for claims ingestion, drug pricing, and CRM sync using BigQuery and PythonDesign production pipelines for batch and streaming workloads, with a particular focus on high-volume claims data and new large-scale data sources on the roadmapArchitect warehouse schemas and transformations with clear separation between raw, staging, and modeled layersMaintain data quality and reliability across systems that feed both human users and AI workloads, including row-count checks, schema drift detection, anomaly alerting, and silent upstream change detectionData GovernanceDesign pipelines to be idempotent and replayable, with raw data always preserved to enable reprocessing when logic changesTrack data lineage across the full lifecycle — origin, transformation, and downstream dependenciesValidate data at every stage before it reaches a dashboard or AI systemReporting InfrastructureBuild reporting systems that give sales, clinical, and leadership teams live visibility into business performanceCreate automated alerting that surfaces meaningful changes in data so the team acts on insights rather than requesting themAI-Ready Data InfrastructureBuild PHI-safe pipelines that support LLM workloads, agent systems, and automationDesign a unified data architecture that connects claims, drug pricing, patient records, CRM activity, and clinical workflows into a coherent wholeOwn ingestion of external data from non-standard formats and sources across a diverse and growing provider baseQualificationsRequired5+ years of experience with Python, SQL, and dbt, with hands-on expertise in BigQuery, Snowflake, or a comparable cloud data warehouse and proficiency with orchestration tools such as Airflow, Dagster, or PrefectDemonstrated experience architecting data platforms, including decisions around batch vs. streaming, incremental vs. full-refresh, and warehouse structureProven ability to build monitoring, lineage tracking, and governance systems that trace data from source to reportExperience using AI tools in day-to-day work and building data infrastructure that AI systems can rely on in productionBackground as an early employee or founding data engineer responsible for building a data stack from the ground upPreferredHealthcare or HIPAA experience; familiarity with ingestion tools such as Fivetran; CRM integrations (Salesforce, HubSpot); or prior experience building data infrastructure for LLM or AI workloadsExperience with streaming frameworks such as Kafka, Pub/Sub, or Flink, or designing systems that handle both batch and real-time data flowsComfort with cloud infrastructure (GCP, AWS) and Linux/sysadmin fundamentals, including VM debugging, log management, and service administrationA bias toward simple, cost-effective solutions — defaulting to open-source and applying sound judgment about when managed services justify their cost and lock-inWhat we offerCompetitive total rewards packages including stock options and benefitsAt Leap, we are committed to providing competitive total rewards packages including stock options and benefits. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations.Equal opportunityLeap is an equal opportunity employer and welcomes applicants from all backgrounds. We’re committed to building a team that reflects a diversity of perspectives, experiences, and identities.
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