Data Engineer

• Ph.D. or masters in computer science, mathematics, statistics, or other field requiring quantitative analysis or equivalent experience

• Three years of financial industry experience

• Experience working with systematic investing teams is a plus

• Programming expertise in Python and C++

• Data analysis experience with R, MatLab, scitoolkit, pytorch, or similar

• Programming skills in SQL

• Strong problem-solving skills

• Effective communication skills

• Strong understanding of modern statistical testing methods

Job Responsibilities

• Monitoring and Analysis

• Accessing quality of all captured market data - both live and historical

• Inventorying all gaps and handling closing gaps via multiple techniques (other internal captures, vendor data, exchange data, etc)

• Extending parsing and identification of other gaps

• Understanding and Documenting session times including holiday schedules

• Comparing day-on-day changes in latency, data rate, bursts, etc

• Research

• Documenting history of exchange microstructure behavior changes

• Understanding and Documenting message timestamp rules for each exchange

• Documenting history of session schedule changes and protocols

• Alpha research and presentation to portfolio managers and trading teams

• Building technology tools to acquire and tag datasets

• Engaging with vendors, brokers to understand characteristics of datasets

• Interacting with portfolio managers and quantitative analysts to understand their use cases

• Building exchange microstructure expertise and helping educate clients as needed

• Analyzing datasets to generate key descriptive statistics

• Cleaning historical data

• Utilizing and maintaining world-class data processing and transformation techniques

Tools

• Building data analysis tools on top of our captured data

• Improving data visualization tools and capabilities

Technical

• Consolidating several PCAPs in a single PCAPNG with meta-data to represent capture location/time across capture regions

• Assessing PTP (Precision Time Protocol) quality within a sole source and across multiple Manage large data sets in a hybrid multi-cloud/on-prem sources environment

• Build performant systems able to analyze terabytes of data quickly

• Enhance firm C++ analytics and expose them in a python environment