5-7 years of hands-on experience in data engineering, focusing on building, optimizing, and maintaining robust ETL/ELT data pipelines.
Strong programming proficiency in Python and advanced SQL development.
Solid hands-on experience with cloud-native data services in Google Cloud Platform (GCP) and/or Microsoft Azure.
Experience with big data processing frameworks such as PySpark or Spark SQL for transformation workloads.
Experience building data pipelines specifically designed to support Machine Learning and AI workloads (e.g., processing structured and unstructured data, feature engineering).
Strong understanding of modern data platform practices, including CI/CD pipelines, Git version control, and Agile delivery methods.
Familiarity with data quality validation, data security (masking, tokenization, access controls), and data governance principles.
Ability to collaborate effectively with cross-functional teams (DataOps, MLOps, Security, and LBU stakeholders) to deliver scalable solutions.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data EngineerEmployement Type: Contract