Data Engineer GCP Consultant developing scalable solutions in GCP. Collaborating with teams to implement data - driven business strategies in the financial services sector.
About the role
- Senior Data Engineer responsible for building and maintaining scalable data ingestion systems for healthcare data. Optimizing data pipelines and collaborating with analytics engineers in a fast-growing environment.
Responsibilities
- The Senior Data Engineer is responsible for building and maintaining scalable data ingestion infrastructure and operational systems.
- Develop and optimize 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.
Requirements
- 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.
Benefits
- Outstanding Team and Culture
- Making a Proven Difference in Healthcare
- Market Opportunity
- Dramatic Growth
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