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Tier-1 brand, mid-level generalist data role, metro hiring, and broad tech stack increase competition.
Data engineering skills are transferable, though finance-specific data and quant collaboration increase domain specificity.
Explicit 3+ years plus many mandatory technologies (SQL, Python, AWS, Spark, ETL) raises strictness.
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Build and maintain high-performance, cloud-based data platforms supporting quantitative research, including back testing and machine learning.
Implement, schedule, monitor, and provide L2 support for ETL/ELT and automation workflows in AWS environment.
Collaborate with technical and quant research teams to design scalable data solutions and improve system performance.
3+ years of applied software engineering experience with formal training or certification.
Bachelor's degree in information systems, information technology, computer science, or similar.
Expertise in SQL and strong experience with AWS services like RDS, S3, Lambda, Secrets Manager.
Strong Python skills including Pandas, Numpy, OOP concepts, and experience with ETL processes and database performance tuning.
Experienced in end-to-end data engineering within cloud environments, preferably AWS, working with large-scale data platforms and data lakes.
Familiar with agile software development and automation including use of AI-assisted development tools within secure engineering standards.
Background collaborating with quantitative researchers to deliver data solutions supporting advanced analytics and machine learning.