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Known employer, metro location, and broad data-engineer skillset combine to raise applicant competition.
Core data engineering skills (SQL, Spark, Python, cloud) transfer easily across industries, so low sensitivity.
Explicit 1–3 years requirement plus specific technical stack expectations creates moderate shortlisting strictness.
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Drive data-product development by collaborating with business teams to gather requirements and enable data-informed decision-making.
Develop and maintain a centralized data-layer using ELT frameworks (preferably DBT) and AWS architecture to deliver scalable, contextualized data models.
Partner with AI/ML teams to provide foundational data structures and ensure delivery of documented, tested, and maintainable code following analytical and quality frameworks.
1-3 years experience in analytical engineering, data modeling, or a similar role.
Proficiency in SQL, SPARK, Python; experience with data engineering tools (AWS, Airflow) and visualization tools (Tableau, Power BI, Looker).
Bachelor's degree preferred in Computer Science, Physics, Math, Data Science, Pharmaceutical Science, or Engineering.
Work Experience Required: 1-3 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in implementing ELT processes with tools like DBT and deploying solutions on AWS environments.
Able to translate business requirements into scalable technical data products with a focus on delivery speed and data accuracy.
Comfortable working in cross-functional teams including AI/ML partners, with knowledge of version control and Agile development methodologies.