Quantitative Data Engineer Job Description Template

The quantitative data engineer owns the data that research depends on: acquisition, cleaning, point in time correctness and delivery. In systematic investing this is the role where errors do the most damage, because a subtle lookahead bias or a badly handled corporate action produces backtests that look excellent and lose money. The work is as much about validation as it is about pipelines.

Typical Duties and Responsibilities

• Build and operate pipelines for market, reference and alternative data
• Guarantee point in time correctness so backtests cannot see the future
• Handle corporate actions, splits, restatements and symbology changes
• Implement automated data quality checks and anomaly detection
• Onboard new vendor data sets and assess their quality before use
• Maintain the security master and symbology mapping
• Optimize storage and query performance for large time series
• Document data lineage, coverage and known limitations
• Investigate and correct historical data issues
• Support researchers with data access and interpretation

Education

• Bachelor’s degree in computer science, engineering, mathematics or a related field

Required Skills and Experience

• 3+ years of data engineering experience, ideally with financial data
• Strong Python and SQL, with experience in large scale data processing
• Understanding of point in time data and survivorship bias
• Experience handling corporate actions and symbology
• Familiarity with time series storage such as kdb+, ClickHouse or Arctic
• Rigorous approach to data validation
• Ability to reason about how data errors would show up in research
• Good documentation practice

Preferred Qualifications

• Experience onboarding alternative data sets
• Exposure to Spark, Dask or another distributed processing framework