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Strong Abbott brand, mid-level generalist Data Engineer title, and typical 5–8 year range increase competition.
Core data engineering skills transfer easily across industries despite pharma exposure preferred.
Explicit 5–8 years and extensive cloud, ETL, and tooling requirements enforce strict filtering.
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Design and manage reusable, cost-effective, high-performance data architectures in AWS cloud environments.
Develop, optimize, and maintain end-to-end data pipelines integrating multiple data sources into data lakes and enterprise warehouses with observability and quality controls.
Provide technical leadership and mentorship to junior engineers while collaborating closely with data science, analysts, and IT teams to enable data-driven decision making in pharmaceutical operations.
5–8 years of data engineering experience preferably in corporate environments; pharmaceutical industry exposure is a plus.
Strong proficiency in Python and SQL; knowledge of Java or Scala is a plus.
Experience with AWS technologies (EMR, Glue, Athena, RDS, Step Functions, S3), Spark, Hadoop, Airflow, dbt, Terraform, Docker, Git, and SQL/NoSQL databases.
Bachelor’s or Master’s degree in computer science, electrical engineering, or related field.
Experienced in designing scalable, secure, and reusable enterprise ETL platforms with emphasis on observability and data quality.
Proven ability to lead and mentor junior data engineers and to collaborate effectively with cross-functional teams including data science and IT in a regulated pharmaceutical setting.
Comfortable applying DevOps best practices such as CI/CD, Infrastructure as Code, and containerization to automate deployment and monitoring of data pipelines and infrastructure.