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Mid-level, metro, popular data engineer role with broad Snowflake/ETL requirements increases competition.
Data engineering skills are broadly transferable, though financial domain experience moderately increases sensitivity.
No explicit years but strong mandatory Snowflake, ETL, and data modelling requirements imply moderate strictness.
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Design, build, and optimize scalable ETL pipelines and dimensional data models on Snowflake for large-scale data processing and analytics.
Ensure high data quality, consistency, availability, and troubleshoot complex pipeline issues including root cause analysis and production support.
Leverage AI-powered tools to enhance data engineering workflows and productivity, while collaborating with architects and business teams to align solutions with enterprise needs.
Strong expertise in SQL including advanced queries, performance tuning, and optimization.
Hands-on experience with Snowflake and relational databases such as SQL Server, Oracle, and PostgreSQL.
Solid understanding and implementation experience of data modelling concepts like star schema, Slowly Changing Dimensions (SCD), and dimensional modelling.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer capable of managing end-to-end data pipelines and models with an emphasis on scalable enterprise solutions and long-term ownership.
Comfortable working at the intersection of data engineering and data architecture, including mentoring junior engineers and collaborating across teams including architects and business stakeholders.
Proficient in integrating AI tools into data engineering tasks to improve productivity and code quality, demonstrating a proactive and problem-solving approach in complex data environments.