Senior Data Engineer

Main Duties & Responsibilities

• Build and Maintain Bronze/Silver Layer Pipelines: Ensure core data sources land accurately, on time, and with full lineage.
• Lead Data Ingestion, Transformation, and Enrichment: Own end-to-end pipelines from raw file landing through cleansed, conformed staging tables, including deduplication, standardization, code mapping, and entity resolution.
• Develop Automated Ingestion Pipelines: Use Snowpipe, Matillion, or custom solutions with reliability, observability, and minimal manual intervention.
• Implement dbt Models: Write clean, tested, documented SQL following medallion architecture patterns and supporting incremental processing at scale.
• Establish Scalable Engineering Patterns: Create reusable templates, macros, and testing frameworks to accelerate onboarding of new data sources.
• Ensure Data Quality and Observability: Implement dbt tests, freshness checks, and anomaly detection. Own pipeline reliability and respond to data quality incidents.
• Collaborate with the Architect and Stakeholders: Translate business requirements into technical specifications, participate in design reviews, contribute to ADRs, and ensure alignment with enterprise standards.

Education and Experience

• Bachelor’s degree in computer science, information systems, engineering, or a related technical field. Advanced degree a plus but not required.
• 5+ years professional data engineering experience, with at least 3 years in modern cloud data environments.
• 2+ years hands-on experience with Snowflake, including performance tuning, Snowpipe, and optimization patterns.
• 2+ years working with dbt (Core or Cloud) in production, including incremental models, testing, documentation, and package management.
• Demonstrated experience with healthcare data. Understanding ICD-10, NPI, NDC codes, and the messy realities of claims data quality.
• Production experience with orchestration tools such as Airflow, Dagster, or similar. Built and maintained DAGs that run reliably at scale.
• Strong SQL skills. Write efficient, readable, well-structured SQL and debug complex query performance issues.
• Python proficiency for data processing, scripting, and pipeline automation.