Principal Data Engineer, Global Liquid Markets-Public Markets and Research

Lead data engineering across four full stack squads, driving data design, best practices, and technical decisions while staying hands-on within one. Build data people can trust: Design and run automated, high-performance data pipelines on Snowflake and AWS that bring market and vendor data into the platform reliably and at scale. Shape the data products: Design the data models behind scalable products that power analytics and applications used in real investment decisions with an AI first mindset. Make quality a standard, not an afterthought: Own the testing strategy, from unit tests built into the pipeline to independent regression testing and set shared quality standards with Core Data and data governance. Drive AI-powered data engineering: Use AI to speed up how the squads build, test, and operate data pipelines, and apply agentic AI to automate repetitive engineering work, with sound guardrails and human review. Turn business needs into a roadmap: Partner with product owners and tech leads to translating what the business needs into a clear, prioritized data roadmap. Raise the engineering bar: Mentor engineers, lead code reviews, and model the practices you want the squads to follow. Bachelor's degree in a technical or business field, or equivalent hands-on experience. 8+ years in data engineering, with a proven record of delivering scalable, high-performing data pipelines and architecture in production. Experience using AI tools to improve the speed, quality, and scale of work; can point to concrete workflows where AI adds value; critically evaluates and validates AI-generated output; and applies AI responsibly, protecting confidential and customer data. Experience with institutional investment or asset management data, ideally in public markets (fixed income, and derivatives) and research (data driven insights from structured and unstructured company, sector, macro sources). Expert SQL, with a strong command of data modeling, warehousing, and architecture, and sound judgment on design tradeoffs. Expert-level experience in designing and architecting scalable, high-performance Snowflake and AWS data solutions, including complex data models, ETL/ELT pipelines, performance optimization, and establishing best practices for data engineering across multiple squads Proficient in Python for transforming, integrating, and automating data workflows. Hands-on experience with AWS services used in data and application workloads, such as S3, Lambda, RDS (PostgreSQL), API Gateway, and IAM. Hands-on CI/CD experience, automating how data work is built, tested, and deployed with tools such as GitHub Actions or similar. Experience building data quality and automated testing into pipelines, from tests that run with every change to regression checks that confirm data products stay correct after releases, using data testing and quality tools such as dbt, iceDQ, or similar. Experience leading technical work, such as guiding design decisions, mentoring engineers, or setting a team's technical direction.