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Tier-1 brand, mid-level generalist role, and metro location increase applicant competition.
Core data engineering skills are broadly transferable across industries despite some finance-specific tooling.
Mandatory 5+ years and strong required tech stack (Databricks, Spark, Python, PL/SQL) make filters strict.
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Drive creation of a scalable, high-quality data platform using modern methodologies like Data Virtualization and Data Domains.
Leverage expertise in data engineering to design and optimize data pipelines, data lakes, data warehouses, and data mesh architectures.
Integrate AI agents and applied AI into data engineering workflows, including configuring MCP servers and employing RAG principles for enterprise data.
5+ years experience in Big Data or enterprise-scale application development using tools like Databricks, Scala, Java, and Python.
Mandatory skills: data engineering and Python proficiency.
Strong experience with data processing (Spark, Starburst, Snowflake, Redshift), storage (Hadoop, MongoDB, Oracle), ETL tools (Airflow, Ab Initio, Talend), SQL, PL/SQL, and Unix Shell scripting.
Bachelor’s degree or equivalent experience.
Experienced in designing and developing distributed systems handling both structured and unstructured data at scale.
Proficient in implementing AI agents with practical knowledge of MCP servers, vector databases, and LLM integration in data engineering.
Skilled at working with complex data architectures including OLTP, ODS, and Data Warehouse applications, with expertise in data quality and lineage.