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Metro location, popular data-engineer title, and broad skill requirements increase applicant competition.
Core data engineering skills are highly transferable across industries despite pharma domain familiarity.
Explicit 1–3 year requirement plus mandatory SQL, Spark, and Python raise filtering to medium.
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Collaborate with business teams to gather requirements and deliver scalable data-products that support data-informed decision-making.
Develop and maintain centralized data-layer using ELT frameworks (preferably DBT) and AWS architecture to transform raw/semi-structured data.
Partner with AI/ML teams to provide data infrastructure, deliver tested and documented code, and accelerate speed-to-delivery for internal analytics solutions.
1-3 years experience in analytical engineering, data modeling, or similar role.
Proficiency with SQL, Spark, and Python; familiarity with DBT preferred but not mandatory.
Experience with data engineering tools (AWS, Airflow), visualization tools (Tableau, Power BI, Looker), and version control systems (Git, SVN).
Bachelor's degree preferred in Computer Science, Physics, Math, Data Science, Pharmaceutical Science, or Engineering.
Experience working in Agile development environments with cross-functional collaboration.
Background in delivering analytical data products supporting AI/ML initiatives and business decision-making.
Ability to translate complex requirements into scalable technical solutions using cloud and modern data engineering tools.