Platform Engineer

The Data Platform team builds and maintains the organization’s enterprise knowledge catalog, knowledge graph, and unified data pipelines. We provide reusable data pipeline tooling for the enterprise and manage the entire Big Query and analytics ecosystem. We focus on data accessibility, semantic connectivity, and automation to improve how data is discovered, processed, and utilized across the company.

You will build AI data agents that interact with our enterprise knowledge catalog, knowledge graph, and extensive analytics infrastructure. This role is responsible for automating complex data workflows, contributing to intelligent data pipeline tooling, and building scalable integrations that bridge our AI capabilities with our core data platforms.

• Agent Development:
  Design, build, and deploy AI data agents to query and navigate the enterprise knowledge graph, data catalogs, and analytics systems.
• Skill Creation:
  Develop modular Agent Skills to execute data retrieval, transformation, and analytical tasks.
• Agent Evaluation:
  Design and implement Agent Evals to test the reliability, accuracy, and factual consistency of agent outputs.
• Data Pipeline & Analytics Integration:
  Build intelligent integrations and reusable tooling that interface with our unified data pipelines and Big Query ecosystems.
• Knowledge Integration:
  Collaborate with data engineers to ingest, curate, and expand the enterprise knowledge catalog and knowledge graph.
• Tool Utilization:
  Leverage Coding Agent tools (Claude, Anti Gravity, and Git Hub Copilot) to accelerate technical delivery and code generation.

• Academic Foundation:
  Bachelor’s Degree (preferably in Computer Science, Information Systems, or a related discipline) accompanied by 1+ years of relevant hands-on engineering experience.
• Programming
  
  Languages:
  
  Strong proficiency in Python and Java.
• AI Frameworks:
  Hands-on experience building AI agents using Google ADK and Lang Chain.
• Agent Tooling:
  Direct experience developing Agent Skills and building Agent Evals.
  AI Assistants:
  Experience utilizing Coding Agent tools (Claude, Anti Gravity, Git Hub Copilot) in the software development lifecycle.
  Execution:
  Ability to work proactively in a team environment and meet delivery timelines.

• Data Systems:
  Experience working with knowledge graphs, enterprise data catalogs, semantic data models, and data pipelines.
• Primary Cloud & Analytics:
  Hands-on experience with Google Cloud Platform (GCP) and deep knowledge of Big Query.
• Git Ops & Orchestration:
  Experience with Git Ops practices and deploying applications to Kubernetes.
• Foundational Models:
  Experience working with Gemini models, Anthropic models, or GPT models.
• Multi-Cloud:
  Experience with other major cloud providers (AWS or Azure).

• Apply strong hands-on development expertise in Java and Python, alongside proficiency in languages such as SQL, Go, Scala and Ruby.
• Accelerate delivery workflows using AI-aided development tools (Copilot, Claude, Gemini CLI, Antigravity CLI) and effective prompt engineering.
• Utilize foundational AI engineering concepts including context engineering, Retrieval-Augmented Generation (RAG) architectures, and AI model evaluation (evals).
• Build and consume robust REST and GraphQL APIs for enterprise data marketplace ecosystems.
• Navigate cloud platform environments (AWS, Google Cloud Platform, or Microsoft Azure), with hands-on Google Cloud experience strongly preferred.
• Enforce cloud security principles, API security protocols, secure coding standards, and established cloud application security frameworks.
• Manage containerized applications using Kubernetes, Docker, and registries such as Google Artifact Registry or…