Data Platform Engineer, Data Infrastructure & Services

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Drive Innovation as our Next Data Platform Engineer - Data Infrastructure Services Ready to participate in building and evolving our next-generation Transportation Logistics data platform on Google Cloud Platform (GCP)? This role combines cloud engineering, data pipelines, and software engineering with a default focus on automation, Infrastructure as Code (IaC), operational resilience, and corporate governance. What Makes This Role Interesting: The Data Platform Engineer – Generalist operates as an AI-augmented builder who decides, designs, and fixes across our cloud infrastructure, platform tooling, and data engineering layers. Recognizing that no single engineer can be a deep expert across every layer of the modern data stack, this role uses AI tools by reflex to deliver end-to-end capabilities with minimal handoffs or coordination overhead. Operating in a lean Platform Engineering pod, you will bring strong core capabilities while maintaining the breadth required to support our ongoing platform migration from AWS and Azure to GCP, build data workflows, and collaborate closely with lead data engineers. Your Key Responsibilities: Decide, Design, Ship End-to-End: Own platform outcomes from intent through to production. Prefer direct iteration (think → build → validate) over cross-role handoffs, pulling in specialists only when risk or complexity truly justifies it. Engineer Infrastructure as Code: Design, provision, configure, and evolve cloud environments using version-controlled, reusable IaC modules (Terraform) and automated CI/CD deployment pipelines. Support Multi-Cloud Migration: Assist in migrating legacy workloads, storage layers, and pipelines from AWS and Azure into our unified GCP platform. Build Data Platform Capabilities: Support the design, deployment, and optimization of data primitives, analytics platforms, and pipeline orchestration (batch and streaming). Embed Governance Guardrails: Translate corporate security, privacy, compliance, and risk requirements into automated technical guardrails (IAM, network controls, secrets management, data classification, and encryption). Maintain Auditability Telemetry: Treat observability, technical documentation, decision records (ADRs), deployment evidence, and runbooks as core elements of the product—ensuring all services are observable and safe to operate. Leverage AI by Reflex: Integrate AI coding assistants and LLM workflows into daily platform operations to accelerate automation, troubleshooting, documentation, and code reviews across multiple technical domains. Cost Capacity Stewardship: Manage cloud spend responsibly through labeling standards, capacity monitoring, cost-control policies, and lifecycle practices. Production-Ready Delivery Standards ("Definition of Done"): IaC CI/CD Managed: Infrastructure fully defined in version-controlled IaC with peer review and automated CI/CD pipeline deployment. Observability Recovery: Monitoring, alerting, logging, and telemetry active prior to release, alongside defined and tested rollback paths. Operational Readiness: Updated runbooks for anything that could create an incident or page someone off-hours. Governance Compliance: Verified alignment with security, data privacy, architecture, and corporate risk controls. Your Essential Skills Experience: Engineering Generalism: Ability to read, reason about, and debug code, data pipelines, and infrastructure across multiple layers of the stack—using AI to execute beyond your primary area of expertise while critically validating output for safety and performance. Cloud Platform Engineering: Solid foundational knowledge of cloud primitives (AWS, Azure, or GCP), with hands-on capability or strong willingness to specialize in GCP. Hands-on experience with declarative IaC tools (e.g., Terraform), state management, and CI/CD pipelines (e.g., GitHub Actions). Data Engineering Fundamentals: Working knowledge of modern data stack primitives and tooling (e.g., BigQuery/Redshift, Airflow/Clou

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