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Early-mid generalist data role, metro location, and broad skill requirements increase applicant competition.
Core data engineering skills (SQL, Spark, Python, Airflow, dbt) transfer easily across industries, so sensitivity is low.
Explicit 1–3 year requirement plus mandatory SQL/Spark/Python and cloud/tool familiarity enforces moderate filters.
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Collaborate with business stakeholders to gather requirements and deliver scalable data-products that enable data-driven decision-making.
Develop and maintain a centralized data-layer using ELT frameworks (preferably DBT) and AWS architectural solutions to efficiently transform data.
Partner with AI/ML teams to provide foundational data structures and ensure quality and timely delivery according to analytical lifecycle processes.
1-3 years of experience in analytical engineering, data modeling, or related role.
Proficiency with SQL, SPARK, and Python is mandatory.
Experience or exposure to DBT and data engineering tools such as AWS and Airflow.
Work Experience Required: 1-3 years; Education Preferred: Computer Science, Physics, Math, Data Science, Pharmaceutical Science, or Engineering.
Experience working within cross-functional teams to translate complex business needs into technical data solutions using modern ELT and cloud technologies.
Ability to deliver well-documented, tested, and reusable code adhering to quality frameworks within agile development environments.
Familiarity with AI/ML concepts and tools for predictive modeling to support analytical product development and innovation.