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Job Description
Structured overview of role & requirementsAbout This Role
Develop and maintain ETL/ELT pipelines and optimize data storage for performance and scalability in a cloud environment.
Collaborate with data architects, analysts, and scientists to support data needs and ensure data quality, security, and integrity.
Drive data engineering initiatives end-to-end including building real-time data ingestion pipelines and automating internal processes.
Minimum Requirements
3-5 years of hands-on data engineering experience, preferably in cloud environments (AWS expertise essential).
Expertise with Databricks, AWS Glue, CloudFormation, GitHub workflows, boto3 APIs, and AWS data engineering ecosystem.
Strong programming skills in Python, PySpark, and experience with SQL and database technologies (MySQL, PostgreSQL).
Work Experience Required: 5+ years in data engineering or software development.
Ideal Candidate Profile
Experienced in designing and operating scalable data pipeline architectures and integrating complex data sets.
Proficient in agile and product-based environments collaborating across global teams.
Familiarity with emerging data platform trends and cloud-native data services, ideally with some knowledge of Life Sciences R&D domain.