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Strong Tier-1 brand, mid-level generalist data role, and metro hybrid context increase applicant competition.
Core data engineering skills transfer across industries, but specialized tools and financial context increase domain specificity.
Explicit 5+ years and many mandatory platform, language, and tooling requirements increase shortlisting strictness.
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Drive creation and scaling of a high-quality, data-intensive platform using data virtualization and domain-driven design.
Develop, optimize, and maintain complex data pipelines, data lakes, and data warehouses for large-scale data processing and analytics.
Design, configure, and integrate AI Agents and Model Context Protocol (MCP) servers to enable autonomous, intelligent data workflows using LLMs and vector databases.
5+ years experience in Big Data or enterprise-scale application development with tools like Databricks, Scala, Java, Python.
Strong proficiency in data engineering technologies: Spark, Starburst/Trino, Snowflake, Hadoop, MongoDB, Oracle, Airflow, and PL/SQL.
Experience designing and managing data pipelines, data virtualization, data federation, data quality, and data lineage processes.
Bachelor’s degree or equivalent experience.
Deep expertise in advanced data engineering including data virtualization, data federation, and distributed systems design.
Practical experience integrating AI agents with data engineering workflows, specifically familiarity with MCP servers and Retrieval-Augmented Generation (RAG) techniques.
Technical proficiency across a broad spectrum from big data platforms to relational databases and AI model tooling in enterprise contexts.