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Tier-1 brand plus metro location but senior niche skillset reduces generic applicant density.
Core data engineering skills are transferable but VP-level finance environment favors industry experience.
Requires 8+ years and mandatory Snowflake Cortex plus Databricks/Spark, making filters stringent.
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Manage development and design of data infrastructure for Data AI platforms focusing on Snowflake, Data Engineering, Data Modeling, Python, SQL/PLSQL.
Develop and maintain data pipelines, ETL processes; optimize data systems for performance and scalability; implement data quality and governance standards.
Collaborate with technology and business stakeholders to translate data needs into technical solutions and provide data-driven insights.
Minimum 8 years of experience in data engineering or related field; typically at least 6 years relevant experience expected.
Expertise in Snowflake Cortex is mandatory; experience with Snowflake and Databricks required.
Proficient in Python, Apache Spark or Hadoop, SQL and NoSQL databases, cloud platforms (AWS, Azure).
Strong understanding of data modeling, warehousing, message queues (e.g., Kafka), version control (e.g., Git), and usage of Jupyter notebooks.
Senior-level data engineer capable of leading architecture design and managing complex data engineering projects.
Experienced with hybrid data environments involving cloud data platforms and modern data engineering toolsets.
Comfortable working collaboratively and independently within a geographically distributed technology team operating in financial services domain.