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Tier-1 bank, popular data-engineer title, and metro role create high competition despite niche Snowflake Cortex requirement.
Core Snowflake/Databricks data engineering skills transferable, but Morgan Stanley finance context raises moderate domain specificity.
Mandatory 8+ years and explicit Snowflake Cortex plus multiple required technologies make shortlisting highly strict.
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Manage, develop, and design data infrastructure for Data AI platforms with emphasis on Snowflake and data engineering.
Develop and maintain data pipelines and ETL processes; optimize data systems for performance and scalability.
Implement data quality and governance standards; collaborate with stakeholders to translate data needs into technical solutions and insights.
8+ years of experience in data engineering or related field; stated expectation of at least 6 years relevant experience.
Must have experience working on Snowflake and Snowflake Cortex.
Proficiency in Python programming and experience with data processing frameworks like Apache Spark or Hadoop.
Experience with SQL/NoSQL databases, cloud platforms (AWS, Azure), data modeling, data warehousing, Kafka, Git, and use of Jupyter notebooks.
Experienced lead-level data engineer with strong expertise in Snowflake and modern data platforms including Databricks and Snowflake Cortex.
Capable of end-to-end ownership of data infrastructure development, including data pipelines, governance, and cross-team collaboration.
Comfortable working independently and across distributed teams with a strong focus on technical excellence and knowledge sharing.