GCP Data Engineer (MLOps)Location: Remote (USA). Preferred: Texas or New JerseyAbout the RoleWe are seeking a GCP Data Engineer with strong MLOps experience to build, scale, and operationalize data and ML pipelines on Google Cloud. You will partner with Data Science, Product, and Platform teams to deliver reliable, production-grade workflows for batch and real-time machine learning, while driving model performance monitoring and operational excellence.Key ResponsibilitiesDesign, develop, and optimize scalable data pipelines and ML workflows on GCP with BigQuery and SparkBuild robust ELT/ETL processes and data models supporting ML feature stores, training datasets, and production inferenceOrchestrate pipelines and jobs, enabling dependency management, retries, and observability (e.g., Airflow)Implement CI/CD and automation for data/ML pipelines, including packaging, versioning, and environment promotionDevelop event-driven and micro-batch processes for real-time ML inference (e.g., via Cloud Functions) and low-latency data preparationEstablish model performance monitoring, drift detection, data quality checks, and alerting dashboardsCollaborate closely with Data Scientists to productionize models and establish reproducible training/inference workflowsEnforce best practices for code quality, testing, documentation, and cost/performance optimization on GCPTroubleshoot production issues, drive root-cause analysis, and implement durable fixes and postmortemsMust-Have QualificationsHands-on experience with Google Cloud (BigQuery) in production environmentsStrong Spark expertise (data processing, optimization, and job orchestration)Advanced proficiency in Python and SQL for data engineering and ML pipeline developmentDemonstrated experience building and supporting production-grade data/ML pipelinesGood-to-Have (Preferred) SkillsGCP services: Airflow, gcloud (CLI), Cloud FunctionsSolid understanding of core ML concepts (training, evaluation, deployment patterns)ML model performance monitoring (data/feature drift, model decay, alerting, dashboards)Explainable AI (xAI) and LLM concepts (prompting, evaluation, guardrails)Real-time machine learning patterns (feature serving, low-latency inference, event-driven architectures)Experience with packaging, testing, and CI/CD for ML (artifact/version management, reproducibility)