Back
Generic senior title, 4–8 year mid-range, and metro location increase competition.
Requires specialized semantic data governance and AI-native tooling, moderately limiting cross-industry fit.
Explicit 4–8 years plus many mandatory tools, governance, and AI-native requirements make filters highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build the core control plane infrastructure for semantic layer and AI-native data platforms managing end-to-end data lifecycle.
Develop automated observability, quality gates, and governance features including RBAC, PII protection, and data lineage to ensure trusted, production-grade semantic data assets.
Enable self-service data product creation through developer portals and templates, advancing AI-native scalability and collaboration with domain experts and data scientists.
4–8 years of experience in data or software engineering focused on data transformation, modeling, or analytics platforms.
Strong proficiency in SQL and at least one general-purpose language such as Python or Scala.
Experience using AI coding assistants (e.g., Claude, GitHub Copilot, Cursor) regularly in development workflow.
Familiarity with data orchestration tools (e.g., Airflow), infrastructure as code (Terraform, Kubernetes), and automated governance platforms (DataHub or similar).
Experienced AI-native engineer proficient with LLM-driven architectures, RAG systems, and agentic workflows for contextual data intelligence.
Strong background in building reliable, quality-first data pipelines with observability frameworks and incident management capabilities.
Skilled in creating scalable, self-service platforms empowering domain teams to autonomously manage trusted data products with compliance and security automation.