Bristol Myers Squibb
Hyderabad, Telangana, India Posted 2d ago
Bristol Myers Squibb is seeking an Analytics Engineer I to help build and support the analytics layer on Databricks that connects curated data to dashboards, self-service BI, and advanced analytics used across the commercial business.
Key Responsibilities
Design, build, and support analytics-ready data products, curated datasets, and reusable transformation assets within a modern lakehouse environment to enable commercial reporting, self-service analytics, and business insights.
Translate business and analytics requirements into clear data specifications, dimensional models, metrics definitions, and dataset readiness criteria aligned to internal modeling and delivery standards.
Develop and maintain scalable SQL- and Python-based transformation logic that integrates structured and semi-structured pharma datasets such as claims, patient, sales, payer, HUB, and specialty pharmacy data.
Apply internally developed accelerators, reusable code patterns, templates, and engineering guardrails to improve development speed, consistency, and maintainability across data products.
Create analytics-friendly models, including fact and dimension tables, cross-domain joins, slowly changing dimensions, and business-rule-driven metrics that support dashboard, reporting, and advanced analytics consumption.
Implement standardized data quality checks, reconciliation logic, validation routines, and anomaly detection controls to ensure datasets are accurate, complete, and trusted by downstream users.
Document data products, metric definitions, lineage, assumptions, and known limitations in accordance with internal documentation and governance standards.
Work as part of a deployed engineering team embedded alongside an assigned Commercial business domain, partnering with data engineers, analysts, BI developers, and business stakeholders to support dashboard and reporting use cases.
Apply data governance standards, access controls, and compliant handling practices for sensitive and regulated data, including PII/PHI awareness.
Requirements
A bachelor's or master's degree in Computer Science, Engineering, Information Systems, Statistics/Mathematics, Analytics, or a related field (or equivalent practical experience).
1–3 years of hands-on experience in analytics engineering, data engineering, business intelligence engineering, or a related role building curated datasets and analytics-ready assets.
Strong proficiency in SQL for analytics transformations, data modeling, validation, and performance tuning.
Working knowledge of Python for data preparation, validation, automation, and analytical workflows.
Experience developing analytics-ready datasets using dimensional modeling concepts such as facts, dimensions, grains, keys, and slowly changing dimensions.
Familiarity with ETL/ELT patterns, lakehouse concepts, and medallion architecture.
Preferred Qualifications
Experience with Databricks, Delta Lake, or lakehouse architecture.
Experience working with commercial pharma datasets such as claims, sales, payer, patient, HUB, or specialty pharmacy data.
Exposure to BI/reporting use cases, semantic layer design, or dashboard-ready data modeling.
