What are the responsibilities and job description for the VP of Data Engineering position at Selby Jennings?
Your Skillset :
- Possession of a Bachelor's degree or higher in Computer Science, Management Information Systems, Business Information Systems, Mathematics, or a related field.
- 7 or more years of experience in data engineering, with a track record of holding senior and leadership roles within peer firms. Proficient in utilizing agile methodologies and modern SDLC processes to deliver high-quality technological solutions transparently and reliably.
- Demonstrated expertise in industry-standard processes throughout the data lifecycle, including automation and tooling for ensuring data quality.
- Extensive domain knowledge in financial datasets, encompassing platforms such as FactSet, Bloomberg, IBES, CompStat, S&P Global, Worldscope, Morningstar, MSCI, Reuters, IDC, Markit, BARRA, Axioma, Northfield, and PORT.
- Proven ability to navigate complex data environments and associated technology and analytics infrastructure, with a history of successfully leading and managing intricate data initiatives or projects. Capable of identifying and communicating business risks to key decision-makers.
- Dedicated to fostering a team culture of accountability and continuous improvement, while actively removing obstacles hindering team success.
- Significant expertise in the Investment Management business domain, spanning areas like research, portfolio management, trading, and investment operations.
- Proficient in delivering datasets from onboarding through mapping and automated data quality processes, facilitating handover to the Data Operations team for ongoing maintenance and utilization by research teams and in production.
- Actively provide data stewardship to team members and Data Operations, ensuring thorough review of datasets for completeness and quality.
- Design and implement robust processes and tools for data onboarding and quality management, aiming to establish an industry best-practice solution for managing the data lifecycle.
- Develop standalone tools for automating data quality checks and fault discovery, enhancing efficiency across teams.
- Mentor junior engineers within the team, aiding in their specialization and contributing to team scalability.
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