Analytics Engineer I , Commercialization

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.

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, and Delta Lake concepts.
• 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, and anomaly detection.
• Working knowledge of governance and secure data handling practices, including documentation, lineage, and access controls.
• Familiarity with BI/visualization tools such as Tableau or Power BI.

Qualifications & Experience

• A bachelors or masters 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.
• Experience transforming large-scale structured and semi-structured datasets using SQL and Python, with attention to data quality and performance.
• Preferred: experience with Databricks, Delta Lake, or lakehouse architecture.
• Preferred: experience working with commercial pharma datasets such as claims, sales, payer, patient, HUB, or specialty pharmacy data.
• Preferred: exposure to BI/reporting use cases, semantic layer design, or dashboard-ready data modeling.