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Strong employer brand, popular data engineering title, and likely metro hiring increase candidate competition.
Core data engineering skills are widely transferable across industries.
Explicit 1-3 years plus required SQL, Spark, Python, AWS and Airflow skills.
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Collaborate with business teams to gather requirements and deliver scalable data-products that enable data-driven decisions.
Develop and maintain a centralized data layer using ELT frameworks (preferably DBT) and AWS architecture to transform raw data into contextualized models.
Partner with AI/ML teams to support AI-enabled tools, ensuring delivery of documented, tested, and efficient data engineering solutions on schedule.
1-3 years of experience in analytical engineering, data modeling, or a similar technical role.
Proficiency in SQL, SPARK, and Python; experience with data engineering tools like AWS and Airflow, and visualization tools such as Tableau, Power BI, or Looker.
Degree preferred in Computer Science, Physics, Math, Data Science, Pharmaceutical Science, or Engineering.
Experience with version control (e.g., Git or SVN) and Agile development methodologies.
Experience working within ELT frameworks, preferably DBT, and familiarity with AWS-based data architectures.
Ability to translate complex business requirements into technical data solutions and work closely with cross-functional teams including AI/ML.
Comfortable operating within Agile development processes and using tools like JIRA, Confluence, and ServiceNow for collaboration.