GCP Data Engineer with Python

Role: GCP Data Engineer with Python Location: Dearborn, MI (4 days a week onsite) Job Type: Contract Experience: Overall 8 to 12 years Job Summary • The Data Engineer will be responsible for supporting the Credit Global Securitization (GS) team’s upskilling initiative by contributing to data engineering efforts across cloud and traditional platforms. • This role is intended to accelerate development and delivery. • The engineer will work closely with cross-functional teams to build, optimize, and maintain data pipelines and workflows using GCP, Python, and ETL tools. Required Technical Skills • Minimum 3+ years of hands-on experience with Google Cloud Platform (GCP), specifically using Astronomer/Composer for orchestration. • Strong proficiency in Python for data engineering and automation. • Experience with RDBMS technologies such as DB2 and Teradata. • Exposure to Big Data ecosystems and distributed data processing. Nice To Have Technical Skills • Prior experience with ETL tools like DataStage or Informatica. Responsibilities • The Data Engineer will play a key role in the developing and maintaining scalable data pipelines and workflows. • The engineer will work with GCP tools like Astronomer/Composer and leverage Python for automation and transformation tasks. • The role involves integrating data from RDBMS platforms such as DB2 and Teradata, and supporting ETL processes using tools like DataStage or Informatica. • The engineer will collaborate with existing team members, including Software Analysts and Scrum Masters, and will be expected to contribute to knowledge sharing and process improvement. Specifically • Develop and implement solutions using GCP, Python, Big Data technologies to enhance data analysis capabilities. • Collaborate with cross-functional teams to design and optimize data models in Teradata and DB2 environments. • Utilize Python for scripting and automation to streamline geospatial data processing tasks. • Integrate and manage data workflows using Cloud Composer to ensure efficient data pipeline operations. • Leverage GCP Cloud to deploy scalable applications and services.