Senior Staff Engineer - Data & ML Ops Platform

This is a Senior Staff-level Data Platform Engineering role to support HEB's enterprise-wide migration and modernization of its data platform onto Google Cloud Platform (Google Cloud Platform). The role is part architect (50%), part technical leader and strategy (30%), and part hands-on engineer (20% coding). The primary objective is helping align and enable 17 separate data teams as they migrate from AWS and legacy platforms to a centralized Google Cloud Platform-based data platform.

They want someone who understands:

•            How modern data platforms are designed

•            Platform architecture and enablement

•            Enterprise cloud migrations

•            Standardization across multiple teams

•            Data platform modernization

•            Technical leadership through influence rather than management

Technical Skills include:

Deep Google Cloud Platform experience

Google Cloud Platform (BigQuery, Cloud Composer/Airflow, Dataflow, Dataproc, GCS) and IaC (Terraform)

MLOps capabilities (Vertex AI, Kubeflow, MLflow)

Python, SQL, dbt

About the Role

We are seeking a visionary Senior Staff Data Platform Engineer to spearhead our next-generation enterprise data modernization. In this role, you will lead the strategic migration of our data estate from AWS/Databricks to Google Cloud Platform (Google Cloud Platform). You will architect centralized, enterprise-grade ingestion, transformation, and serving frameworks, while scaling automated self-service tooling and our production MLOps ecosystem.

Core Responsibilities    

Cloud Migration & Modernization: Architect and execute the enterprise data platform migration from AWS/Databricks to a modern, centralized Google Cloud Platform stack.

Enterprise Frameworks: Design and build standardized, reusable ingestion, transformation, and data serving layers with built-in governance and quality controls.

Developer Experience & Tooling: Create self-service automation tooling to empower Data Engineers, Analysts, and Data Scientists with frictionless workflows.

MLOps Architecture:       Lead the technical roadmap for MLOps platforms—scaling feature stores, automated CI/CD pipelines for models, and robust inference infrastructure.

Technical Strategy & Mentorship: Set technical standards, define architectural guardrails, and mentor senior engineering talent across the organization.

Required Qualifications & Experience

Experience: 10+ years of data engineering/architecture experience, including 3+ years operating at a Staff or Senior Staff level.

Cloud & Architecture: Deep, hands-on architectural expertise in Google Cloud Platform (BigQuery, Cloud Composer/Airflow, Dataflow, Dataproc, GCS) and Infrastructure as Code (Terraform).

Platform Modernization: Demonstrated success leading large-scale, multi-cloud or legacy data migrations with minimal operational disruption.

MLOps Capabilities: Proven experience building production MLOps platforms (Vertex AI, Kubeflow, MLflow, feature stores, model registry, monitoring).

Programming & Data: Advanced proficiency in Python, distributed computing frameworks (Spark), and modern data modeling (SQL, dbt).

Leadership: Track record of driving cross-functional alignment across Data Engineering, Data Science, and Analytics stakeholders.

Preferred Qualifications             

Legacy Ecosystems: Working knowledge of AWS data services and Databricks / Delta Lake environments.

Modern Data Stack: Experience with Apache Iceberg, open table formats, and Kubernetes-based orchestration.

Domain Experience: Experience architecting high-throughput, mission-critical data platforms in enterprise environments.