Data Engineer Google Cloud Platform & Vertex AI

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Dice is the leading career destination for tech experts at every stage of their careers. Our client, Hexacorp, is seeking the following. Apply via Dice today!<br><br><strong>No Remote for Data Engineer- Only Onsite<br><br></strong>We are seeking a <strong>Data Engineer with expertise in Google Cloud Platform (Google Cloud Platform) and Vertex AI</strong> to design, build, and optimize <strong>data pipelines</strong> supporting machine learning and analytics. The role requires hands-on experience in <strong>data engineering, ML-ready data preparation, and integration with Vertex AI pipelines</strong> for scalable AI/ML model development.<br><br><strong>Key Responsibilities<br><br></strong><ul><li>Design and implement scalable ETL/ELT pipelines using Dataflow, Dataproc, BigQuery, and Pub/Sub.</li><li>Collaborate with Data Scientists and MLOps teams to prepare and serve ML-ready datasets for training and inference on Vertex AI.</li><li>Integrate structured, semi-structured, and unstructured data from multiple sources into Google Cloud Platform data lake/warehouse.</li><li>Build feature pipelines and manage Vertex AI Feature Store.</li><li>Implement data quality checks, governance, and lineage in pipelines.</li><li>Optimize storage and compute costs across Google Cloud Platform services.</li><li>Support real-time and batch data processing for ML pipelines and analytics.</li><li>Ensure security, compliance, and monitoring of data pipelines.<br><br></li></ul><strong>Required Skills & Experience<br><br></strong><ul><li>Strong expertise with Google Cloud Platform data services: BigQuery, Dataflow (Apache Beam), Pub/Sub, Dataproc, Cloud Storage, Composer (Airflow).</li><li>Experience working with Vertex AI pipelines and Feature Store.</li><li>Strong SQL and Python programming skills.</li><li>Hands-on experience with data modeling, partitioning, performance optimization.</li><li>Proficiency with CI/CD for data pipelines (Cloud Build, Jenkins, GitHub Actions).</li><li>Familiarity with Terraform/IaC for Google Cloud Platform environment setup.</li><li>Knowledge of containerization (Docker, Kubernetes) for pipeline orchestration.<br><br></li></ul><strong>Preferred Qualifications<br><br></strong><ul><li>Google Cloud Platform Certifications: Professional Data Engineer or Professional Machine Learning Engineer.</li><li>Experience with Kubeflow, MLflow, or TFX for pipeline integration.</li><li>Exposure to data observability tools (Dataplex, Great Expectations, dbt).</li><li>Strong understanding of AI/ML lifecycle workflows and model deployment integration.<br><br></li></ul><strong>Why Join Us?<br><br></strong><ul><li>Work on data & AI-driven solutions at scale.</li><li>Collaborate with a global team of Data Engineers, MLOps Engineers, and Data Scientists.</li><li>Opportunity to grow into a Lead Data Engineer / MLOps Architect role.</li></ul>

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Or you may receive even more specific questions, such as: What's on your calendar? Do you plan blocks of time to do certain types of work? Do you have an open calendar that everyone can see?...

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