Experience: 7 10+ years in Data Engineering / Cloud Development
Proven experience designing and building scalable data pipelines (ingestion, transformation, validation) using AWS, Python, and Databricks (Spark/PySpark)
Experience working with structured, semi-structured, and unstructured data
Exposure to life sciences data formats such as DICOM, FASTQ/BAM/PLINK, or SAS7BDAT is strongly preferred
Hands-on experience with modern data platforms such as Databricks
Experience with data governance tools (e.g., Immuta) is preferred
Familiarity with analytics and statistical tools such as Tableau, R/Posit, or SAS is a plus
Deep expertise in AWS data services (e.g., S3, Lambda, RDS, FSx/EFS) and cloud-native architecture best practices
Strong programming skills in Python and SQL, with experience in PySpark; working knowledge of R is a plus
Experience with CI/CD and orchestration tools such as GitHub, Azure DevOps, and Apache Airflow
Working knowledge of data science and machine learning concepts (e.g., scikit-learn, TensorFlow, PyTorch)
Experience with Databricks, including cluster usage and performance optimization; exposure to Unity Catalog and platform administration is a plus
AWS and/or Databricks certifications are preferred
Experience with Kubernetes/EKS is a plus
Experience architecting technology solutions to meet business requirements
Experience managing technology projects end-to-end through planning, design, build, testing, and deployment phases
Understanding of Computer System Validation for GxP vs. Non-GxP technologies is preferred
Strong communication skills and ability to work collaboratively with internal IT partners, business partners, and external vendors

Cloud Data Developer / Engineer - Hybrid

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