GCP Data Engineer

Overview

Senior Data Engineer to build enterprise data foundations for an AI-powered conversational analytics capability, enabling governed querying by LLM workflows.

What you'll do

• Design, build, and maintain scalable data pipelines for conversational analytics use cases.
• Develop and maintain semantic layers, business logic mappings, and data structures for AI understanding.
• Model and transform complex business data into query-optimized structures for insight production.
• Partner with AI and software teams to support LLM workflows, agent orchestration, and governed access patterns.
• Implement ingestion, transformation, and data quality processes for structured analytical datasets.
• Support both live-query and cached data access patterns based on agreed architecture and performance needs.
• Contribute to production readiness via documentation, testing, monitoring, and knowledge transfer.

What you'll need

• 4+ years of experience in data engineering in cloud-based analytical environments.
• Strong hands-on experience with SQL and Python for data processing and transformation.
• Experience building scalable pipelines and transformation workflows for large, complex datasets.
• Strong understanding of data modelling, semantic layer design, and analytical data structures.
• Experience with GCP services such as BigQuery, Pub/Sub, Cloud Run, or Vertex AI.
• Experience with cloud data platforms such as Databricks or similar.
• Understanding of governed data access, role-based permissions, and enterprise data standards.

Nice to have

• Familiarity with semantic modelling for NLP, conversational analytics, or AI-driven querying.
• Understanding of prompt-aware data design or retrieval-augmented architectures.
• Experience working in regulated enterprise environments with strong governance requirements.
• Experience contributing to knowledge transfer and internal capability enablement.

Details

• Work closely with AI engineers, software engineers, data scientists, and DevOps teams.
• Support deployment into controlled Dev, Test, and Production environments.