Job Description & Details
This is a serious enterprise-grade role for someone who has lived and breathed GCP data and AI infrastructure for a decade. You will be walking into an environment requiring heavy lifting on data migrations, complex pipeline orchestration, and production-ready machine learning architectures. If you enjoy untangling legacy setups and designing performant, scalable cloud data ecosystems, this gig is worth a serious look.
What You'll Actually Be Doing
Your day-to-day will be a mix of high-level architecture design and hands-on guidance. You'll spend a lot of time leading data migrations off legacy setups, optimizing massive BigQuery data warehouses for both speed and cost, and building resilient data pipelines using tools like Dataflow and Cloud Composer. Beyond data engineering, you are expected to collaborate heavily with data scientists to get models deployed properly via Vertex AI, while keeping an ironclad grip on security, IAM governance, and infrastructure as code.
The Core Tech Stack
Google Cloud Platform is non-negotiable here, and you need deep fluency in its data ecosystem. BigQuery schema design, partitioning, and cost optimization are absolute table stakes. You'll also need solid production experience with Dataflow, Dataproc, Pub/Sub, and Cloud Composer for orchestration. On the AI side, Vertex AI is heavily leaned upon for machine learning deployments, and you'll need Terraform to manage your infrastructure cleanly.
Interview Expectations
Expect the interview panel to grill you on large-scale BigQuery optimization and cost containment strategies, likely asking you to whiteboard a scenario where a poorly written query is blowing up a client's cloud budget. They want to see if you instinctively understand slot reservation models, partitioning strategies, and clustering. You'll also likely be quizzed on Vertex AI deployment patterns and how you handle state management and drift using Terraform during complex cloud migrations.
Application Advice
Make sure your resume screams GCP certification and scale. They explicitly require a valid Google Cloud Professional Architect certification, so put that right at the top of your summary. When detailing your past roles, emphasize quantifiable metrics like terabytes or petabytes migrated, pipeline throughput improvements, and concrete cloud cost savings you've achieved using Terraform and BigQuery optimizations.