Join Google Cloud as a Data Analytics Customer Engineer, a pivotal role where you'll be the leading technical expert for your assigned accounts. You will collaborate closely with Sales Specialists to drive the technical pre-sales strategy, acting as the trusted technical advisor for Chief Data Officers, CTOs, Lead Architects, and developers. Your responsibilities will include conducting proactive discovery sessions to understand complex, legacy customer data environments and assessing their readiness for migration to Google Cloud Platform (GCP).

You will be instrumental in designing, coding, and deploying robust, end-to-end Data + AI pipelines that address significant enterprise challenges. This involves architecting modern open lakehouse solutions and showcasing how unified data foundations empower deterministic, hallucination-free conversational AI through code demonstrations.

Leveraging your expertise in value-selling, you'll perform detailed Total Cost of Ownership (TCO) analyses and architect solutions to optimize costs. You'll guide customers in adopting modern, secure data engineering practices that integrate data, analytics, and AI seamlessly. This highly technical position offers substantial growth opportunities for individuals with deep knowledge in Python, PySpark, and distributed data systems, capable of translating technical proficiency into business-transforming migrations.

Develop and execute technical strategies for major accounts, engaging with C-level executives through live demonstrations to resolve business issues using the Agentic Data Cloud.

Design, develop, and deploy production-ready Data + AI pipelines for applications such as fraud detection and recommendation engines. Map existing data estates to modern, open-format architectures like Apache Iceberg.

Implement intent-driven data engineering practices to autonomously deploy PySpark pipelines utilizing the Data Agent Kit, Antigravity, and the Model Context Protocol.

Lead impactful technical showcases and value validation exercises using BigQuery, Borderless Lakehouse, Knowledge Catalog, Spark, and Vertex AI to demonstrate tangible business outcomes.

Conduct comprehensive Total Cost of Ownership (TCO) assessments and optimize architectures, including resource allocation like DRAM/SSD, compute instance types, and token usage, to manage costs effectively on the Lightning Engine.

A Bachelor's degree or equivalent practical experience is required.

Minimum of 6 years of experience in roles such as a data engineer, systems engineer, solutions architect, or pre-sales consultant.

Demonstrated experience delivering compelling demos, conducting workshops, and providing architectural overviews to business leaders.

Proven ability to migrate, refactor, or debug workloads, whether proprietary or open-source.

Experience in building robust data platforms, data warehouses, or data lakes. Proficiency in data programming languages and experience with agentic platforms are essential.

Excellent English communication skills are necessary for managing stakeholder relationships effectively.