BlueSprig

Compensation

Full Description

The Senior Data Engineer owns the design, implementation, and maintenance of our data architecture supporting clinical operations across 160+ ABA therapy clinics. This role builds the data foundation that powers clinical quality measurement, operational analytics, payor reporting, and value-based care delivery—infrastructure that directly impacts patient outcomes and business decisions. This position will design and implement ETL pipelines moving data from source systems (EMR, scheduling, billing, finance) into a data warehouse, create dimensional models for analytics, and build tools with team members to make data accessible to clinical and business stakeholders. This is hands-on deep technical work. The Senior Data Engineer will be fluent in Python, able to optimize SQL queries, implement data quality guardrails, and collaborate directly with clinical operations, finance, and IT leadership to understand requirements and deliver solutions. Success in this role means delivering reliable, well-documented data infrastructure that non-technical stakeholders can trust and use to make better decisions about patient care and operations. Responsibilities Job Duties and Responsibilities include the following. Other duties may be assigned. Build ETL Pipelines: Design, implement, and maintain robust data pipelines moving data from source systems (EMR, scheduling, billing) into our data warehouse. Data Architecture: Own the design and evolution of our data warehouse structure. Data Modeling: Create and maintain dimensional models, fact tables, and aggregated views for analytics and reporting. Data Quality: Implement validation, monitoring, and quality checks to ensure data integrity across clinical and operational datasets. Tooling and Automation: Build internal tools and automations to improve data accessibility for non-technical stakeholders. Documentation: Maintain clear technical documentation for data models, pipelines, and processes. Collaboration: Work directly with clinical operations, finance, and business stakeholders to understand requirements and deliver solutions. Participate in product design discussions, providing input on usability, workflows, and automation opportunities. Qualifications Education/Experience: ​Bachelor's degree from four-year college or university; or one to two years related experience and/or training; or equivalent combination of education and experience. Skills: 5+ years of hands-on data engineering experience Python expertise: Strong proficiency in Python for data processing, ETL, and automation SQL & PostgreSQL: Deep knowledge of SQL and PostgreSQL administration, optimization, and data modeling Data Modeling: Proven experience designing dimensional models, star schemas, and data warehouses Data Quality: Experience implementing data validation, testing, and quality frameworks Version Control: Proficient with Git and collaborative development workflows. ETL Tools: Experience with modern data pipeline frameworks (Airflow, dbt, Dagster, or similar). Healthcare Data: Understanding of healthcare data challenges (PHI/PII handling, HIPAA, compliance, clinical data structure