leap - Senior Data Engineer

• 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