Back
Strong employer brand, mid-level generalist data role, metro location, and broad tech requirements increase competition.
Core data engineering skills transfer across industries, though biotech/regulatory experience is preferred.
Explicit 5–9 years requirement plus mandatory Databricks/Spark/Python/AWS skills make filters highly stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, test, and maintain scalable ETL/ELT data pipelines and integration solutions for large and complex datasets.
Optimize and secure data workflows using Databricks, Apache Spark, Python, SQL, and cloud technologies, ensuring data availability for analytics, reporting, and AI use cases.
Collaborate with data architects, business teams, data scientists, and DevOps across development, deployment, and production support activities.
5–9 years of relevant professional experience in data engineering or related fields.
Hands-on experience with Databricks, Apache Spark, PySpark, Spark SQL, Python, SQL, and cloud platforms such as AWS.
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
Experience designing, developing, and supporting production ETL/ELT pipelines, including workflow orchestration and data security compliance.
Experienced working with large, complex datasets in regulated industries like biotechnology, pharmaceuticals, life sciences, or manufacturing.
Proficient in performance tuning of Spark and SQL workloads, data modeling, lakehouse architecture, and governance controls.
Comfortable working in Agile environments with cross-functional, global teams and contributing to CI/CD, automated testing, and operational support.