Lead the design and implementation of scalable data platforms (data warehouses, data lakes) and end-to-end data pipelines
Develop and optimize production-grade code (Python/Spark) for data transformation and financial analysis
Build customized analytical applications, dashboards, and quantitative investment tools
Ensure efficient delivery and adherence to data governance and integrity standards
Collaborate with client investment teams to translate investment process requirements into robust, automated tools and data products
Provide technical and project leadership across engagements
Understand client business context to ensure technical solutions solve real problems

Requirements

7+ years of hands-on experience in a technical or analytical role (Quant, Quant Dev, Data Scientist, or Data Engineer). Minimum of 5 years for highly exceptional candidates
Prior experience at hedge funds, fintech firms/startups, market makers, or top-tier asset management firms. Experience at top technology or strategy consulting firms considered on a case-by-case basis
Demonstrated project leadership, ideally overseeing technical delivery from inception to production
Proficiency in Python (Pandas, NumPy, Scikit-learn) and advanced SQL query optimization
Experience with at least one major cloud platform (AWS, GCP, or Azure) and associated data services (Snowflake, Databricks)
Deep understanding of data modeling and data warehousing principles
Familiarity with data pipeline orchestration tools (Airflow, Dagster, Prefect)
Experience building quantitative trading or investment tools
Background in financial data (market data, portfolio analytics, fund accounting)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSparkSQLPandasNumPyScikit-learndata modelingdata warehousingdata transformationquantitative trading

Soft Skills

project leadershipcollaborationproblem-solvingcommunication