Analytics Engineer I, Commercialization

Overview

Build and support the analytics layer on Databricks that connects curated data to dashboards, self-service BI, and advanced analytics across a Commercial business domain. Develop well-modeled datasets, metrics, and semantic assets while working under the guidance of senior engineers.

What you'll do

• Design, build, and support analytics-ready data products, curated datasets, and reusable transformation assets within a modern lakehouse environment.
• Translate business and analytics requirements into data specifications, dimensional models, metrics definitions, and dataset readiness criteria.
• Develop and maintain scalable SQL- and Python-based transformation logic for structured and semi-structured pharma datasets, including claims, patient, sales, payer, HUB, and specialty pharmacy data.
• Apply internally developed accelerators, reusable code patterns, templates, and engineering guardrails.
• Create fact and dimension tables, cross-domain joins, slowly changing dimensions, and business-rule-driven metrics.
• Implement data quality checks, reconciliation logic, validation routines, and anomaly detection controls.
• Document data products, metric definitions, lineage, assumptions, and known limitations.
• Partner with data engineers, analysts, BI developers, and business stakeholders on dashboard and reporting use cases.
• Apply data governance standards, access controls, and compliant handling practices for sensitive and regulated data, including PII/PHI awareness.

What you'll need

• 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.
• Experience transforming large-scale structured and semi-structured datasets using SQL and Python, with attention to data quality and performance.
• 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.
• Understanding of Git/version control, code reviews, and basic CI/CD concepts.
• Strong problem-solving and communication skills, with the ability to work effectively with technical and business stakeholders.
• Willingness to work as part of a deployed team embedded within a business domain and adapt to domain-specific priorities.

Nice to have

• 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.

Details

• Location: Hyderabad, Telangana, India.
• The role is part of a deployed engineering team embedded alongside an assigned Commercial business domain.