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Data Engineer

Karnataka, India (On-site)

Data Engineer

1 Month ago • 3 Years + • Data Analyst

About the job

7 skills required for this role

Test your skills to join the top 1% applicants for this job

cpp

data-visualization

python

pytorch

sql

matlab

linux

Job Description

This Data Engineer role within Millennium's latency-critical trading group involves building and maintaining a low-latency data platform for systematic portfolio engines. Responsibilities include monitoring and analyzing market data quality, identifying and resolving data gaps, documenting exchange microstructure behavior and message timestamp rules, conducting alpha research, building data acquisition tools, collaborating with vendors and portfolio managers, performing data cleaning and transformation, and developing data analysis and visualization tools. The ideal candidate will possess strong programming skills (Python, C++, SQL), data analysis expertise (R, MatLab, etc.), and a deep understanding of statistical testing methods. The role requires handling large datasets in a hybrid cloud environment and building high-performance analytical systems.

Must have:

PhD or Master's in relevant field or equivalent experience
3+ years of financial industry experience
Python and C++ programming expertise
Data analysis experience (R, MatLab, etc.)
SQL programming skills
Strong problem-solving skills

Good to have:

Experience with systematic investing teams
Experience with PCAP, PCAPNG, PTP

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About the job

Millennium is a global investment management firm, built on a sophisticated operating system at scale. We seek to pursue a diverse array of investment strategies, and we empower our employees to deliver exceptional outcomes and enable our portfolio managers to do what they do best – navigate the markets.

Job Description

We are building a world-class systematic data platform which will power the next generation of our systematic portfolio engines.

The latency critical trading group is looking for a Data Scientist/Engineer to join our growing team. The team consists of low latency linux engineers, network engineers, datacenter engineers, and C++ engineers who are responsible for building our low latency stack.

This is an opportunity for individuals who are passionate about quantitative investing. The role builds individual’s knowledge and skills in four key areas of quantitative investing, namely: data, statistics, technology, and financial markets.

Desirable Candidates

Ph.D. or master's 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

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