Analytics Engineer II, Commercialization

Overview

Build and own analytics-layer transformations, semantic models and metrics on Databricks for assigned Commercial business domains, working as part of a deployed engineering team embedded with that domain.

What you'll do

• 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 the 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.

What you'll need

• Strong proficiency in SQL and Python for analytics transformations, data modeling, and validation.
• Hands-on experience with Databricks (notebooks, jobs/workflows).
• 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.

Details

• Location: Hyderabad, Telangana, India.
• Work model: the ad does not state a work mode for this role.