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Sr Data Engineering - GCP

Posted April 10, 2026
Full-time Mid-Senior Level

Job Overview

We are looking for a GCP Data Engineer to design, build, and maintain scalable data platforms on Google Cloud. The ideal candidate will have strong experience in automated data ingestion, analytics engineering, and MLOps, with the ability to support marketing analytics use cases and BI-driven decision making.

You will work closely with Data Science, Analytics, and Business teams to enable data-driven insights through robust pipelines, models, and reporting solutions.

  • Design, build, and maintain scalable data pipelines to prepare complex business data for conversational analytics use cases.
  • Develop and maintain semantic layers, business logic mappings, and data structures that improve AI understanding of client taxonomies, KPIs, hierarchies, and business concepts.
  • Model and transform complex business data into structures optimised for query generation, interpretation, and insight production.
  • Partner with AI and software engineering teams to support LLM workflows, agent orchestration, and governed access patterns.
  • Implement robust ingestion, transformation, and data quality processes for structured analytical datasets.
  • Support live-query and cached data access patterns depending on agreed architecture and performance needs.
  • Ensure data is accessible, explainable, and aligned to business definitions used in evaluation and user acceptance testing.
  • Collaborate with Data Scientists to support ground-truth evaluation, validation datasets, and regression testing.
  • Work with architects and client data stakeholders to align designs with enterprise data standards, governance requirements, and long-term maintainability.
  • Contribute to production readiness through documentation, testing, monitoring, and knowledge transfer to internal teams.
  • Support deployment of data solutions into controlled Dev, Test, and Production environments.
  • Help shape scalable patterns for future expansion into additional datasets and more advanced analytical capabilities.

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