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Tier-1 brand, metro location, mid-level generalist data role with broad required skills increases applicant competition.
Data engineering skills are highly transferable across industries despite finance domain knowledge preference.
Moderate technical filters; specific cloud, SQL and Python skills expected but no strict years or certifications.
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Own and deliver specific components of data pipelines for ingestion and transformation within the broader data platform.
Execute and monitor data quality validation processes and troubleshoot basic data pipeline issues.
Develop and maintain documentation, write efficient SQL and Python scripts, participate in code reviews, and adhere to engineering best practices.
Experience working with cloud-based data platforms such as Snowflake or equivalent and AWS services like S3 and Glue.
Proficiency in Python and SQL for data processing and transformation.
Familiarity with RDBMS databases (Oracle, Postgres, MSSQL), ETL concepts, and orchestration tools (e.g., Airflow, Control-M).
Work Experience Required: Not explicitly mentioned in the JD.
Senior-level data engineer capable of owning and delivering distinct subsections of a complex data platform or applications.
Experience implementing solutions based on technical specifications within a data engineering team environment.
Comfortable working in asset management or investment management data domain with willingness to learn related concepts.