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Generalist early-career analytics role, strong employer brand and metro hiring increases applicant competition.
Core analytics engineering skills are transferable, though pharma data and PII/PHI experience increases domain bias.
Explicit 1–3 year requirement and mandatory SQL/Python/Databricks/data-modeling skills raise screening strictness.
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Develop and maintain analytics-ready, dimensional data products and transformation logic in a modern lakehouse environment to support commercial reporting and business insights.
Translate business and analytics requirements into data specifications and models aligned to internal standards, ensuring accuracy, security, and reusability.
Apply data quality, reconciliation, documentation, and governance practices while collaborating with data engineers, analysts, and stakeholders to support dashboard and reporting use cases.
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 related roles involving analytics-ready datasets and data modeling.
Proficiency in SQL and working knowledge of Python for data transformation and validation.
Not explicitly mentioned: specific notice period or mandatory onsite location, but role involves working with commercial pharma datasets and modern lakehouse technologies (Databricks, Delta Lake).
Experience working with large-scale structured and semi-structured pharmaceutical data such as claims, sales, payer, patient, HUB, or specialty pharmacy datasets.
Familiarity with data lakehouse architecture, Medallion architecture layers, dimensional modeling, and semantic layer concepts for BI and dashboard consumption.
Ability to implement standardized data quality and governance practices for sensitive and regulated data, including PII/PHI awareness, and collaborate effectively across technical and business teams.