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Mid-level generalist analytics role, metro location, and broad SQL/Python/Databricks requirements create high applicant competition.
Core analytics engineering skills are transferable, though pharma data domain preference increases domain specificity moderately.
Explicit 1–3 year requirement plus mandatory SQL, Python, Databricks, and data modeling raises shortlisting strictness.
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Develop and maintain analytics-ready data products and curated datasets to support commercial reporting and business insights in a modern lakehouse environment.
Translate business and analytics needs into dimensional models, SQL/Python transformation logic, and metric definitions ensuring accuracy, security, and reusability.
Implement data quality controls, documentation, governance, and collaborate with stakeholders to drive adoption and continuous improvement of data products.
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 experience in analytics engineering, data engineering, BI engineering, or related role building curated datasets and performing data transformations using SQL and Python.
Proficiency in SQL and working knowledge of Python for data transformation and validation.
Experience or familiarity with dimensional modeling, data quality practices, and governance for regulated/sensitive data such as PII/PHI.
Experience with Databricks, Delta Lake, and lakehouse architecture including medallion layers (bronze, silver, gold/refined).
Familiarity with commercial pharmaceutical datasets (claims, sales, payer, patient, HUB, specialty pharmacy) and related metrics.
Ability to bridge technical and business conversations, partnering effectively with engineering, analytics, and business stakeholders.