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Mid-level, common senior engineer title but specialized AI-native and data-platform skills limit broad applicant pool.
Role needs domain-specific data-platform, governance, and LLM/RAG experience, moderately transferable across industries.
Explicit 4–8 years requirement plus mandatory tools and governance/AI-native experience creates strict filtering.
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Design, build, and maintain the control plane infrastructure orchestrating data lifecycle operations including ingestion, transformation, and distribution.
Develop observability frameworks and automated quality gates to ensure built-in data trust and reliability.
Create self-service portals and automation for governance policies like RBAC and PII protection to enable domain teams to autonomously manage trusted data products.
4–8 years experience in data engineering or software engineering focused on data transformation, modeling, or analytics platforms.
Proficiency in SQL and at least one general-purpose programming language (Python or Scala).
Demonstrated experience using AI coding assistants like Claude, GitHub Copilot, or Cursor routinely in development workflows.
Experience with data orchestration tools (e.g., Airflow) and modern DevOps tools (Terraform, Kubernetes, CI/CD automation).
Experienced in AI-native engineering including LLM-driven architectures, RAG systems, and agentic workflows with vector/graph databases.
Expertise in building reliable, production-grade data pipelines emphasizing data quality, observability, and automated governance using platforms like DataHub.
Skilled at enabling developer autonomy through building self-service tools and portals within a cross-functional, enterprise data platform environment.