Analytics Engineer II, Commercialization

About the Role

The Commercial Data & AI HYD team is seeking an Analytics Engineer II to build and own the analytics-layer transformations that connect curated data to dashboards, self-service BI, and advanced analytics used across the business. This role operates as part of a deployed engineering model, working closely with an assigned Commercial business domain (e.g., Sales, Marketing, Market Access, or Medical Affairs) to keep the analytics layer aligned with how that part of the business operates. The successful candidate will analyze business requirements and build, test, and integrate application components, tools, and utilities that support commercial reporting and analytics consumption, while owning the transformation logic, dimensional models, and metrics definitions for assigned data domains on the Databricks Lakehouse.

Key Responsibilities

• Design, build, and own analytics-layer transformations, semantic models, and metrics for assigned data domains, turning curated data into trusted, self-serve analytics products on Databricks.
• Analyze business requirements and build, test, and integrate application components, tools, and utilities that support commercial reporting and analytics consumption.
• Build analytics-ready data models — star/snowflake schemas, conformed dimensions, fact tables — that turn commercial business logic into consistent, reusable structures.
• Apply performance and cost-optimization practices for Databricks workloads within assigned pipelines, including cluster configuration and Delta Lake optimization (Z-ordering, partitioning, OPTIMIZE/VACUUM).
• Develop and maintain SQL- and Python-based transformation logic that integrates structured and semi-structured commercial datasets (sales, claims, patient, payer, HUB, specialty pharmacy).
• Implement data quality checks, validation routines, and reconciliation logic for assigned datasets, following established testing and documentation standards.
• Document data products, metric definitions, lineage, and known limitations in accordance with internal governance standards.
• Work as part of a deployed engineering team embedded with an assigned Commercial business domain, partnering with analysts, BI developers, and business stakeholders to support dashboard and reporting use cases.
• Apply data governance standards and access controls for sensitive and regulated data, including PII/PHI awareness.
• Provide guidance to Analytics Engineer I team members on data modeling and engineering best practices.

Requirements & Tech Stack

• Strong proficiency in SQL and Python for analytics transformations, data modeling, and validation.
• Hands-on experience with Databricks (notebooks, jobs/workflows) and working knowledge of Delta Lake concepts (ACID tables, Z-ordering, partitioning).
• Solid experience with dimensional data modeling — star/snowflake schemas, conformed dimensions, fact tables, SCD handling.
• Familiarity with lakehouse/medallion architecture concepts (bronze/silver/gold layers).
• Working knowledge of cloud data engineering fundamentals (e.g., AWS storage, compute, and IAM concepts).
• Experience with data quality practices, including profiling, validation, and reconciliation.
• Working knowledge of software engineering practices including Git/version control, code reviews, and CI/CD.
• Familiarity with BI/visualization tools (Tableau, Power BI) and a clear sense of what makes a dataset genuinely self-service.
• Strong problem-solving and communication skills, with the ability to work independently on well-defined analytics domains.
• Comfort working as part of a deployed team embedded within a business domain, adapting to domain-specific priorities.