Software Engineer

Location: Bangalore, Karnataka, India

Department: Information Technology

About Millennium
Millennium is a global investment management firm built on a scalable, technology-driven platform. We run a diverse set of investment strategies and empower our teams to deliver exceptional outcomes by providing world-class tools, infrastructure, and data.

Team & Role Overview
The Latency-Critical Trading team is building a best‑in‑class systematic data platform to power the next generation of low‑latency systematic strategies. The team includes low‑latency Linux, network, datacenter, and C++ engineers focused on our end‑to‑end trading stack.

Key Responsibilities

• Monitor and assess the quality of live and historical market data; detect, inventory, and remediate data gaps.

• Maintain and document exchange session times, holiday schedules, timestamp rules, and protocol/microstructure changes.

• Analyze latency, data rates, bursts, and message flows to understand microstructure behavior and system performance.

• Clean, transform, and manage an inventory of large‑scale datasets (including PCAP/PCAPNG) in a hybrid cloud/on‑prem environment.

• Build and improve tools for market data capture.

• Work with vendors and brokers to assess and provision datasets.

• Build and improve tools for data analysis, visualization, and diagnostics on top of captured market and network data.

• Enhance and extend C++ analytics libraries and expose them within a Python environment for systematic research and alpha development.

• Collaborate closely with portfolio managers, quantitative researchers, and engineers to translate trading use cases into robust data and tooling solutions.

Qualifications

• Bachelor’s or Master's in Computer Science, Mathematics, Statistics, Engineering, or another quantitative field, or equivalent experience.

• 3+ years of experience in financial markets, electronic trading, or high‑frequency / systematic environments preferred.

• Strong programming skills in Python, C++, and SQL; experience with R / MATLAB / SciPy stack / PyTorch or similar tools for data analysis.

• Solid understanding of modern statistical testing methods and comfort working with large, noisy, real‑world datasets.

• Experience with Linux, large‑scale data processing, and preferably network data (PCAP, timestamping, PTP) and low‑latency systems.

• Strong problem‑solving skills, attention to detail, and effective communication with both technical and non‑technical stakeholders.