Senior Data Engineer (GCP)

Remote - US - Full-time !!Applicants must be authorized to work in the United States!! Looking for a Senior GCP Data Engineer to design, build, and optimize our scalable data pipelines and modern data infrastructure. In this role, you will own our end-to-end data processing frameworks - handling ingestion, modeling, and workflow automation; while ensuring high reliability, performance, and cost-efficiency. You will also collaborate closely with stakeholders to prepare, model, and govern data pipelines tailored for machine learning and agentic AI tooling applications. What You'll Do: - Pipeline Architecture & Development: Design and build scalable data pipelines using GCP services (Cloud Dataflow, Cloud Pub/Sub, Datastream, Spark, and Cloud Composer) to ingest, process, and store large datasets from diverse sources. - Data Modeling & ETL: Develop, test, and maintain robust data models, schemas, and ETL processes utilizing BigQuery, Cloud SQL, Spark, and related reporting tools. - Stakeholder Collaboration: Partner with cross-functional stakeholders to translate business requirements into efficient, scalable data infrastructure. - Performance & Cost Optimization: Optimize data pipelines for throughput, scaling, and cost efficiency using GCP-native tools like Dataproc and Bigtable. - Data Governance & Security: Implement validation checks, enforce data governance policies, access controls, and security best practices to protect data integrity. - Workflow Orchestration: Automate and orchestrate workflows using Cloud Composer (Apache Airflow) to guarantee reliable, scheduled operations. - Reliability & Monitoring: Own pipeline and data infrastructure health end-to-end—proactively monitoring, alerting, troubleshooting, and conducting root cause analysis. - ML & AI Data Preparation: Prepare, structure, and expose clean datasets for machine learning and agentic AI use cases, ensuring production-ready pipelines for AI applications. - Technical Guidance: Provide senior-level technical direction, establishing best practices for pipeline design, data architecture, and infrastructure standards. What You'll Bring: - Experience: 5+ years of professional data engineering experience, with at least 3 years focused on cloud environments (Google Cloud Platform preferred). - GCP Expertise: Deep, hands-on experience across core GCP services including BigQuery, Dataflow, Pub/Sub, Cloud Composer, Cloud Storage, Cloud Functions, and Cloud SQL. - SQL Mastery: Advanced SQL skills with real-world experience writing and optimizing complex queries for performance. ETL & Orchestration: Proven background in custom ETL development and workflow orchestration using Apache Airflow / Cloud Composer. - Programming: Strong proficiency in Python and/or Spark for data transformation and pipeline development. Data Streaming & Batch: Comprehensive experience with both real-time streaming pipelines (Pub/Sub, Dataflow) and batch processing architectures. Data Warehousing: Solid conceptual grasp of modern data warehousing, large-scale dataset tuning, and query optimization. - Governance & Security: Working knowledge of cloud data governance, compliance, and security standards. - AI/ML Readiness: Understanding of ML data requirements and data preparation workflows, with a strong interest or background in agentic AI tooling. - Soft Skills: Excellent problem-solving instincts, keen attention to detail, and clear, confident communication abilities. Nice to Haves: - Bachelor's degree in Computer Science or a related technical field (or equivalent practical experience) - Active GCP Certification (Professional Data Engineer or Professional Cloud Architect) - Experience setting up ML infrastructure on GCP (e.g., Vertex AI / AI Platform, TensorFlow) - DevOps & MLOps familiarity: CI/CD, Infrastructure as Code (Terraform), and containerization (Kubernetes, Docker) on GCP - Background working within supply chain, logistics, or large-scale distributed enterprise environments