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Mid-level data engineering role in a metro with broad AI/vector requirements creates high applicant competition.
Data engineering skills transfer broadly, but RAG/vector specialization and regulated-industry preference increase sensitivity.
Explicit 6–8+ years plus mandatory data, vector DB, cloud and ETL skills makes shortlisting strict.
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Design and optimize scalable ETL/ELT data pipelines and vector infrastructure to support enterprise AI, including Retrieval-Augmented Generation (RAG).
Build and maintain high-performance vector databases and semantic layers enabling autonomous AI agents to access and analyze complex corporate data efficiently.
Lead deployment and maintenance of CI/CD pipelines for data infrastructure, ensuring data quality, governance, and real-time data availability in hybrid cloud environments.
Bachelor’s degree in Computer Science, Software Engineering, or related field (or equivalent experience).
6-8+ years of experience in software engineering and AI data solutions with enterprise-grade implementations and customer-facing consulting exposure.
Strong skills in data pipeline development with tools like Glue, Databricks, Synapse, or Dataproc and proficiency in relational and NoSQL databases (e.g., PostgreSQL, DB2, MongoDB).
Proficiency in SQL and Python programming and expertise in ETL/ELT tools (Airflow, dbt, Kafka) and cloud platforms (AWS, Azure, GCP).
Experienced data engineer capable of architecting complex AI data platforms focused on autonomous agents and retrieval-augmented generation solutions.
Skilled in managing vector databases and semantic data structures to optimize AI-driven data retrieval with strong emphasis on performance and scalability.
Familiar with data governance, testing, CI/CD processes, and collaborating closely with architects, software engineers, and data scientists in fast-paced innovation cycles.