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Mid-level generalist data engineer in a metro location attracts high applicant competition.
Core data engineering skills are transferable, but semantic/LLM experience raises domain specificity to medium.
Explicit 4+ years plus mandatory GCP, SQL, Python, and semantic modelling makes shortlisting highly strict.
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Design and maintain scalable data pipelines to prepare complex business data for AI-powered conversational analytics.
Develop semantic layers and business logic to enhance AI understanding of client taxonomies and KPIs for accurate querying.
Collaborate with AI Engineers, Software Engineers, Data Scientists, and DevOps to support LLM workflows, governed data access, and production readiness.
4+ years of experience in Data Engineering, preferably in cloud-based analytical environments.
Proficient in SQL and Python for data processing and transformation.
Experience with GCP services such as BigQuery, Pub/Sub, Cloud Run, or Vertex AI, and cloud data platforms like Databricks.
Experience with data modelling, semantic layer design, and managing large enterprise datasets; understanding of governed data access and role-based permissions.
Experienced in building scalable, governed data solutions supporting AI/ML/LLM use cases and complex analytical workflows.
Demonstrates strong collaboration with multidisciplinary teams to align data engineering with AI and business requirements.
Operates effectively in regulated enterprise environments with attention to documentation, testing, and long-term maintainability.