This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Senior Data Engineer is responsible for building and maintaining scalable data ingestion infrastructure and operational systems. You focus on the "EL" portion of our ELT stack, collaborating closely with analytics engineers. You are an expert at building robust, configuration-driven systems and event-driven processes that handle large enterprise datasets. This role requires deep experience in incremental data migration from sources like RDS and DynamoDB into Snowflake using tools like Kinesis or Airbyte. You are comfortable with containerization and committed to building maintainable toolsets that the broader engineering team can utilize. What You'll be Doing: - Developing and optimizing scalable data ingestion pipelines from platform sources (RDS, DynamoDB) into Snowflake. - Building event-driven pipelines using Kinesis, Airbyte, or other open-source frameworks to handle high-volume healthcare data. - Implementing and maintaining a staging-layer architecture that supports the broader medallion (staging → intermediate → marts) structure. - Creating configuration-driven, containerized toolsets (Docker/Kubernetes) to ensure data solutions are portable and maintainable. - Ensuring data reliability by building comprehensive monitoring, alerting, and automated testing for all ingestion processes. - Collaborating with analytics engineers to streamline the flow of data for dbt transformation. - Applying software engineering best practices, including modular design and test-driven development, to all data infrastructure. - Refactoring existing ingestion processes to improve performance, cost-efficiency, and scalability. - Mentoring mid-level and junior engineers through code reviews and sharing best practices in data operations. Qualifications - 4-6+ years of professional experience in data engineering with a focus on data ingestion and infrastructure. - Proficiency in Python and SQL, with a track record of building production-grade data pipelines. - Strong experience with ingestion tools such as Kinesis, Airbyte, Kafka, or similar frameworks. - Hands-on experience with Snowflake and moving data from operational databases (RDS, DynamoDB) to cloud data warehouses. - Solid understanding of AWS services (S3, Lambda, Step Functions, RDS). - Experience with containerization (Docker) and deploying maintainable systems. - Knowledge of ELT patterns, specifically supporting analytics engineering workflows and dbt. - Experience with CDC (Change Data Capture) and incremental processing methodologies. - Detail-oriented mindset regarding data privacy and compliance (HIPAA experience is a plus). - Strong communication skills, with the ability to collaborate effectively across data science and engineering teams. Requirements - Ability to pass a background check. - Must live in and be eligible to work in the United States. Benefits - Dynamic role with opportunities to contribute to the company's growth and shape its future. - Unparalleled learning and growth prospects, collaborating closely with experienced Clinicians, Engineers, Software Architects, and Digital Health Entrepreneurs. - Salary range for this position is: $150,000-170,000/year. Remote Work Requirements - An established private work area that ensures information privacy. - A stable high-speed internet connection for remote work. - This role is remote, but you will be required to come to on-site meetings multiple times per year. This may be in the interview process, onboarding, and team meetings.

• Build the Semantic Layer: Design and implement production-grade dbt models that transform raw source data into clean, documented, and tested Data Marts. • Enforce Data Quality: Move beyond basic NOT NULL checks. Implement semantic validation rules (using dbt tests or Great Expectations) that catch business logic failures before they hit the dashboard. • Own the Pipeline: Manage the lifecycle of your data models from IDE to production, including code review, CI/CD integration, and performance tuning in Snowflake. • Bridge the Gap: Collaborate with Platform Engineers to understand upstream ingestion patterns (Python/Fivetran) and debug data issues at the source. • Design the core Star Schemas and data models that define our business. • Act as the primary interface between Clinical Operations and Engineering. You will interview stakeholders to uncover the "Why" behind their requests and translate vague business needs into precise technical specifications. • Mentorship & Standards: Set the standard for SQL style, dbt macro usage, and testing rigor. Mentor intermediate engineers on healthcare domain nuances (e.g., why a reversal claim behaves differently than a void).

• Lead and/or participate in requirements gathering and analysis sessions • Design and build relational databases • Develop strategies for data acquisitions, archive recovery, and implementation of databases • Work in a data warehouse environment • Translate business needs into data architecture solutions • Define, design, and build dimensional databases • Develop data warehousing blueprints

• Design and implement scalable, performant data models to deliver rapid insights into drivers of drug prices and cost-savings opportunities. • Develop and optimize processes to improve the correctness and usability of 3rd party data. • Implement data quality principles to raise the bar for reliability of data shared internally and externally. • Implement and enforce data governance policies to ensure PII/PHI protection, security, and compliance. • Apply AI best practices to document data engineering processes, data models. • Be a role model and mentor to junior team members by providing technical guidance and support.