Back
Tier-1 brand, popular analytics engineer title, and Hyderabad metro location increase candidate competition.
Core SQL/Python analytics skills are transferable but pharma datasets and PHI handling increase domain specificity.
Explicit 1–3 years in analytics engineering plus required SQL/Python and Databricks familiarity make filters moderately strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and support analytics-ready data products and curated datasets within a modern lakehouse environment for commercial reporting and business insights.
Translate business requirements into dimensional data models, metrics definitions, and scalable SQL/Python transformation logic, focusing on structured and semi-structured pharma datasets.
Implement data quality controls, documentation, and governance practices while partnering with stakeholders to ensure data products are accurate, reusable, and aligned to business needs.
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Statistics/Mathematics, Analytics, or related field (or equivalent experience).
1–3 years of hands-on experience in analytics engineering, data engineering, or business intelligence roles building curated datasets and analytics-ready assets.
Strong proficiency in SQL for data transformations and modeling; working knowledge of Python for automation and validation.
Experience or familiarity with Databricks, Delta Lake, lakehouse architecture, and pharma-related datasets preferred but not strictly mandatory.
Experience working with commercial pharmaceutical datasets such as claims, sales, payer, patient, HUB, or specialty pharmacy data, understanding common identifiers and integration challenges.
Comfortable developing dimensional models including fact and dimension tables, slowly changing dimensions, and business-rule-driven metrics for analytics consumption.
A technical collaborator capable of applying engineering best practices such as version control, code reviews, reusable code patterns, and data governance in a regulated environment.