Senior GCP Data Engineer Location: Remote - Wisconsin based client
Job Type: Contract with potential hire
Rate: $68.00-$73.00 on W2 basis - no sponsorship available at any time
NO C2C HELP in ANY form About the Role We are seeking a Senior GCP Data Engineer to design, build, and scale robust data infrastructure across our Google Cloud Platform (GCP) ecosystem. In this role, you will take ownership of end-to-end batch and streaming ETL/ELT pipelines, transforming complex raw data into high-performing, governed datasets that power enterprise analytics, BI, and machine learning models. Key Responsibilities

• GCP Data Architecture: Design and optimize scalable cloud architectures using BigQuery, Cloud Storage, and Dataproc/Dataflow to support enterprise analytical needs.
• ETL/ELT Pipeline Development: Build, test, and maintain resilient batch and real-time data pipelines using Python and SQL, orchestrating workflows with Apache Airflow / Cloud Composer.
• Performance & Cost Optimization: Continuously monitor and tune BigQuery query performance, pipeline execution speed, and GCP infrastructure cost efficiency.
• Data Governance & Quality: Implement automated data quality checks, access controls, data lineage, and metadata management using Data Catalog.
• DevOps & SDLC Standard: Drive software engineering best practices including containerization (Docker, Kubernetes), CI/CD integration, and formal SDLC/ADLC policies.
• Cross-Functional Collaboration: Partner with Data Architects, BI Specialists, Analysts, and Business Stakeholders to deliver scalable data products that drive decision-making.

• 8+ years of dedicated experience in Data Engineering building scalable, enterprise platforms.
• Must-Have GCP Expertise: Proven hands-on experience building production data platforms using Google Cloud Platform services.
• Must-Have Orchestration: Strong expertise with Apache Airflow (or Cloud Composer).
• Advanced proficiency in SQL and Python for data transformations and pipeline engineering.
• Strong foundation in data warehousing (dimensional modeling, star schema), distributed systems, and modern cloud ETL/ELT architecture.

• Deep experience across the full GCP data stack (BigQuery, Dataflow, Dataproc, Cloud Storage, Data Catalog).
• Experience with Docker, Kubernetes, and CI/CD deployment pipelines.
• Knowledge of supporting machine learning feature stores and ML model deployment workflows.
• Experience creating formal SDLC/ADLC policies, data governance frameworks, and security standards.