Data Engineer responsible for developing research analytic data infrastructure at Sutter Health. Involves managing data quality, pipelines, and compliance with healthcare regulations.
About the role
- Data Engineer specializing in digital transformation at Bounteous. Collaborating on data solutions and migration projects with a focus on data integrity and quality.
Responsibilities
- Pipeline Migration
- Logic & Scheduling: Refactoring and migrating extraction logic and job scheduling from legacy frameworks to the new Lakehouse environment.
- Data Transfer: Executing the physical migration of underlying datasets while ensuring data integrity.
- Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements
- Consumption Pattern Migration
- Code Conversion: Translating and optimizing legacy SQL and Spark-based consumption patterns (raw and modeled) for compatibility with Snowflake and Iceberg.
- Usage analysis: Understand usage patterns to deliver the required data products.
- Stakeholder Engagement: Acting as a technical liaison to internal clients, facilitating "handoff and sign-off" conversations with data owners to ensure migrated assets meet business requirements.
- Data Reconciliation & Quality: A rigorous approach to data validation is required. Candidates must work with reconciliation frameworks to build confidence that migrated data is functionally equivalent to that already used within production flows.
Requirements
- Education: Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.
- Experience: Minimum of 5 years of professional "hands-on-keyboard" coding experience in a collaborative, team-based environment. Ability to trouble shoot (SQL) and basic scripting experience.
- Languages: Professional proficiency in Python or Java.
- Methodology: Deep familiarity with the full Software Development Life Cycle (SDLC) and CI/CD best practices & K8s deployment experience.
- Core Data Engineering Competencies: Candidates must demonstrate a sophisticated understanding of the following modeling concepts to ensure data correctness during reconciliation:
- Temporal Data Modeling: Managing state changes over time (e.g., SCD Type 2).
- Schema Management: Expertise in Schema Evolution (Ref: Iceberg Apache) and enforcement strategies.
- Performance Optimization: Advanced knowledge of data partitioning and clustering.
- Architectural Theory: Balancing Normalization vs. Denormalization and the strategic use of Natural vs. Surrogate Keys.
- Technical Stack Requirements: Extraction & Logic: Kafka, ANSI SQL, FTP, Apache Spark Data Formats: JSON, Avro, Parquet Platforms: Hadoop (HDFS/Hive), Snowflake, Apache Iceberg, Sybase IQ. Candidate will also need to work with our internal data management platform, and must have an aptitude for learning new workflows and language constructs is essential.
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