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Strong employer brand, mid-level data engineer role, metro location, and broad required skills increase competition.
Core data engineering skills transfer across industries, but AI-native governance and vector tooling require domain adaptation.
5+ years plus mandatory Spark/Databricks, vector DB, Azure, and CI/CD requirements imply high strictness.
Architect and lead AI-native data pipelines for ingestion, transformation, and serving optimized for analytics and AI consumption.
Build and own the semantic data layer and KPI factory to provide standardized, trusted metrics for AI and business use.
Govern data acquisition strategies and agent data access, ensuring auditability and responsible AI-human collaboration on Azure platform.
Bachelor’s Degree in related technical field or equivalent experience.
At least 5+ years’ experience in Big Data solutions development using Spark and Databricks, with AI/ML or AI-assisted workflows experience.
Proficient in Python (PySpark), SQL, and hands-on with vector databases, embedding pipelines, and RAG design.
Experience with Azure Data Factory, Airflow, CI/CD pipelines, data lineage, auditability, and governance for AI data consumption.
Experienced in designing Big Data architectures including Data Warehousing, Lakehouse, Data Mesh, and semantic layer design oriented towards AI/ML use cases.
Skilled in integrating AI-native operating model concepts into data pipeline and access design, supporting safe AI-human workflows.
Able to collaborate cross-functionally with engineering, governance, and business teams to deliver high-quality, audited, and explainable data infrastructure for AI applications.