Lead Data Platform Engineer, AI & Cloud (Google Cloud)

Locations: Irving, TX (preferred) • Charlotte, NC • Phoenix, AZ • Minneapolis, MN • San Antonio, TX • Des Moines, IA • NJ • CA
Conversion: Yes

About the Role

We are seeking a Lead Cloud Data Platform Engineer to help design, build, and operationalize a next‑generation, AI‑enabled data ecosystem on Google Cloud. In this role, you will shape modern data and AI capabilities supporting a large‑scale Cyber Security data environment. You will contribute to the migration from on‑premises systems to a hybrid Google Cloud architecture while leading innovation across data engineering, automation, and AI technologies.

You will collaborate with principal engineers, product managers, and other data teams to develop robust data solutions, automation frameworks, and scalable pipelines that unlock advanced analytics across the organization.

If you enjoy building modern cloud-native systems, working with large data environments, and pioneering AI-driven tooling, this role is for you.

Responsibilities

As a Lead Cloud Data Platform Engineer, you will:

AI & Automation

• Build and operationalize AI‑enabled data capabilities on Google Cloud to support ingestion, transformation, and distribution for large-scale analytics.
• Use AI/agentic technologies (e.g., LangChain, LangGraph/ADK, RAG, GraphRAG, MCP) to automate:
  • Data pipelines
  • Data quality and governance
  • Metadata management
  • Compliance workflows

Data Engineering & Cloud Architecture

• Design and implement cloud-native data pipelines using Python, PySpark, Kafka, Airflow/Cloud Composer, and DataProc.
• Develop and support real-time data flows using Kafka, Flink, and Spark streaming.
• Build data products within data lakehouse architectures on Google Cloud Storage and BigQuery.
• Drive the adoption of modern engineering patterns and scalable data processing frameworks.

Collaboration & Leadership

• Partner with product managers, architects, and engineering teams to define roadmaps, prioritize deliverables, and execute against strategic goals.
• Mentor and guide engineers across multiple teams in cloud, data, and AI best practices.
• Contribute to the modernization of the Cyber Security Data Ecosystem across hybrid cloud environments.

Required Qualifications

• 5+ years of data engineering experience in large-scale or cloud-native environments.
• 3+ years of hands-on experience with data lakehouse systems using:
  • Python, PySpark
  • Kafka
  • Airflow or Cloud Composer
  • Google Cloud Storage, BigQuery, DataProc
• Demonstrated experience building Spark‑based ingestion and processing pipelines.
• Recent, hands‑on experience with AI/agentic tools, including:
  • LangChain
  • LangGraph / ADK
  • RAG and GraphRAG
  • MCP for agent-based data automation
• Experience working in large, complex data environments.

Desired Qualifications

• Experience using AI to automate code generation for data engineering (e.g., prompt engineering, context optimization).
• Strong knowledge of cloud-native data lakes, warehouses, and end‑to‑end automated pipelines.
• Public cloud certifications, such as:
  • GCP Professional Data Engineer
  • Azure Data Engineer
  • AWS Data Analytics Specialty
• Experience with React and Node.js for UI development (preferred but not required).

Additional Details

• Role Type: Senior Data Ops Engineer / Lead Information Security Engineer (P4)
• Environment: Large-scale data and cloud engineering environment
• Must Haves:
  ✅ Large data environment experience
  ✅ Strong Google Cloud background
  ✅ Strong Python and Kafka experience
  ✅ Strong AI tooling experience

Apply now