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Popular data-engineer role with common tech stack and recognizable pharma brand, creating moderate applicant competition.
Core data engineering skills (SQL, Spark, Python) transfer easily across industries, so background sensitivity is low.
Explicit 1-3 year requirement plus mandatory SQL/Spark/Python/AWS/Airflow skills drives strict shortlisting.
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Collaborate with business stakeholders to gather requirements and deliver scalable data-products that enable data-informed decision-making.
Develop and maintain a centralized data-layer using ELT frameworks (preferably DBT) and cloud architectures (preferably AWS) to transform raw data into contextualized models.
Partner with AI/ML teams to provide foundational data structures for AI-enabled internal tools, ensuring delivery of documented, tested, and efficient code following quality frameworks.
1-3 years of experience in analytical engineering, data modeling, or a similar role.
Proficiency in SQL, SPARK, and Python programming languages.
Knowledge of data engineering tools such as AWS and Airflow, and visualization tools including Tableau, Power BI, or Looker.
Preferred educational background in Computer Science, Physics, Math, Data Science, Pharmaceutical Science, or Engineering area of study.
Experience working in scalable data engineering environments, with familiarity or exposure to DBT and cloud platforms like AWS.
Ability to translate complex technical data requirements into actionable solutions collaborating across business and technical teams.
Comfortable working within Agile development processes, version control systems (Git/SVN), and familiar with AI/ML concepts and predictive modeling.