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Tier-1 brand, popular data engineer title, and broad required skillset increase competition.
Core data engineering skills transfer across industries, though financial domain knowledge adds moderate specificity.
Multiple mandatory technical skills plus explicit 8+ years requirement make filters highly stringent.
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Manage, develop, and design data infrastructure and ETL pipelines for Data AI platforms, focusing on Snowflake and related technologies.
Optimize data systems for performance, implement data quality and governance standards, and collaborate with stakeholders to translate data needs into technical solutions.
Lead efforts in documentation, knowledge sharing, code reviews, and contribute to the broader data architecture community.
8+ years of experience in data engineering or related field; typically 6+ years to meet skill expectations.
Mandatory experience with Snowflake Cortex, Snowflake, Databricks, Python, SQL and NoSQL database systems, and data processing frameworks like Apache Spark or Hadoop.
Familiarity with cloud data platforms such as AWS or Azure and their data services.
Experience with data modeling, data warehousing concepts, message queues/streaming platforms (e.g., Kafka), version control (e.g., Git), and Jupyter notebooks.
Senior data engineering professional capable of leading data infrastructure design and development with strong Snowflake Cortex expertise.
Experience operating in complex, distributed teams with cross-functional collaboration between technology and business stakeholders.
Proven track record managing scalable data pipelines and enforcing data quality/governance in enterprise environments.