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Tier-1 employer, metro role, and broad Data Engineer title increase applicant density despite niche semantic specialization.
Core data engineering and semantic-layer skills are transferable, though telecom KPI domain knowledge increases preference for industry fit.
Multiple mandatory specialized technologies and semantic-layer expertise raise strict technical filtering for candidates.
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Own and evolve the schema registry and domain glossary for core operational data and KPIs.
Manage the NL-to-SQL pipeline including accuracy benchmarking, query validation, and trust scoring.
Deploy and maintain semantic metric layers and MCP servers for cross-agent metric resolution and BI tool integration.
Advanced SQL skills with expertise in cloud data warehouses such as BigQuery, Snowflake, Databricks, or Redshift.
Experience with semantic layer and data modeling tools like dbt, LookML, Cube.dev, or equivalents.
Proficiency in NL-to-SQL techniques, Python data engineering, and BI tool integration at API/embedded level.
Work Experience Required: Not explicitly mentioned in the JD.
Strong experience with MCP server implementation and semantic metric layer design patterns.
Familiarity with telecom domain concepts including network KPIs, operational data schemas, and CDR/EDR structures is highly desirable.
Capable of managing complex data contracts and schema change processes end-to-end in a cloud environment.