Analytics Engineer I

Job description

**Working with Us** Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us . **Position Summary** We are looking for an early ‑ career Analytics Engineer to help develop trusted, analytics ‑ ready data products that enable commercial reporting, business insights, and decision support. In this role, you will transform raw and curated data into well ‑ modeled datasets, metrics, and semantic assets that are easy for analysts and business users to consume. You will apply data modeling, SQL transformation, data quality, documentation, and governance practices while partnering closely with engineering, analytics, and business stakeholders to ensure data products are accurate, secure, reusable, and aligned to business needs. **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, quality, 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, consistent, and trusted by downstream users. - Document data products, metric definitions, lineage, assumptions, reusable components, and known limitations in accordance with internal documentation and governance standards. - Partner with data engineers, analysts, BI developers, and business stakeholders to support dashboard/reporting use cases, drive adoption of published data products, and continuously improve reusable delivery standards. Apply data governance standards, access controls, and compliant handling practices for sensitive and regulated data, including PII/PHI awareness. **Skills & Competencies** - 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 including bronze, silver, and gold/refined layers. - Hands-on familiarity with Databricks notebooks, jobs/workflows, Delta Lake concepts, and curated data product development. - Understanding of semantic layer concepts, metric definitions, and how curated datasets support BI, dashboards, and self-service analytics. - Experience with data quality practices including profiling, cleansing, standardization, reconciliation, anomaly detection, and issue triage. - Working knowledge of governance and secure data handling practices including documentation, lineage, access controls, and PII/PHI awareness. - Familiarity with BI/visualization tools such as Tableau or Power BI and the data structures required for effective reporting consumption. - Understanding of engineering practices such as Git/version control, code reviews, modular development, and basic CI/CD concepts. Strong problem-solving, communication, and stakeholder collaboration skills, with the ability to bridge technical and business conversations. **Qualifications & Experience** - 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 related roles building curated datasets, data models, and analytics-ready assets. - Experience transforming large-scale structured and semi-structured datasets using SQL and Python, with attention to data quality, business logic, performance, and maintainability. - Preferred: experience with Databricks, Delta Lake, lakehouse architecture, and refined/gold-layer data product development. - Preferred: experience working with commercial pharma datasets such as claims, sales, payer, patient, HUB, or specialty pharmacy data and understanding common identifiers, metrics, and integration challenges. - Preferred: experience in Medical Evidence Generation and the Medical data domain, including familiarity with evid