Lead Data Architect

MA CAPITAL U.S. LLC; Chicago (IL); Hybrid; USD 120,000 - 150,000; Full time

Benefits offered by this job

Comprehensive health coverage

401(k) Retirement Plan

Flexible working options

Job summary

A proprietary trading firm is seeking an Architect (On-prem) Data Engineer to define and build the firm's data strategy and core data platform. This senior, hands-on role involves end-to-end ownership of the data lifecycle, including live data ingestion and storage solutions. Candidates should have at least 8 years of experience in data infrastructure and proficiency in Python and SQL. The role offers a hybrid work model based in Chicago along with comprehensive health coverage and a 401(k) retirement plan.

Qualifications

• 8+ years designing and building data infrastructure in data-intensive environments.
• Strong proficiency in Python and SQL.
• Experience with high-throughput, event-driven systems.

Responsibilities

• Define the data architecture and operating model.
• Design and build high-throughput data pipelines.
• Own the full data lifecycle from ingestion to downstream consumption.

Skills

Data infrastructure design

Python

SQL

Event-driven systems

High-throughput data processing

Tools

Kafka

Spark

Flink

Job description

MA Capital US LLC is a proprietary trading firm specializing in systematic and high-performing discretionary strategies across multiple asset classes. We leverage advanced technology, quantitative research, and sophisticated models to capitalize on opportunities in global markets. Our culture is built on innovation, efficiency, and transparency, providing our professionals with the tools and flexibility to succeed.

Position Overview

We’re seeking an Architect (On-prem) Data Engineer to define and build the firm’s data strategy and core data platform. This is a founding, senior, hands‑on role responsible for establishing the framework for live data ingestion, at‑rest storage, archival, and downstream consumption across research and trading systems.

This role has end‑to‑end ownership of the data lifecycle and requires balancing architectural direction with hands‑on delivery. The role is 70% hands‑on engineering and 30% design and strategy, comfortable operating independently, making pragmatic trade‑offs, and building systems without over‑engineering.

Key Responsibilities

Data Architecture & Strategy

• Define the data architecture and operating model spanning live ingestion, at‑rest datasets, and long‑term archival.
• Establish a data lifecycle management framework, including storage tiers, processing patterns, and retention policies.
• Design and maintain centralized data repositories (data lake / warehouse) optimized for large‑scale quantitative research and analytics.
• Set standards for data formats, schemas, partitioning, and lifecycle management, balancing performance, cost, and scalability.

Data Infrastructure

• Design and build high‑throughput, fault‑tolerant data pipelines for market data & analytics workloads.
• Develop scalable ETL/ELT workflows supporting real‑time, near‑real‑time, batch, and replay‑based research pipelines.
• Implement storage solutions optimized for large‑scale, time‑series and event‑driven datasets.
• Develop core abstractions and tooling that allow new datasets and features to be added without reworking foundational systems.
• Establish Infrastructure as Code and automation patterns to ensure reliability, reproducibility, and operability as a largely solo resource.

End-to-End Data Ownership

• Own the full data lifecycle from ingestion through downstream consumption by research and trading systems.
• Establish standards for data quality, validation, reproducibility, and operational reliability.
• Implement monitoring, alerting, and operational controls for data pipelines.
• Maintain clear documentation and conventions to ensure consistent usage across teams.
• Work closely with traders, researchers, and engineers to translate strategy requirements into robust data systems.
• Enable rapid onboarding of new strategies by delivering reliable historical and live datasets.
• Support ongoing model development by providing extensible pipelines for feature generation and experimentation.

Required Qualifications

• 8+ years designing and building data infrastructure or distributed systems in data‑intensive environments; experience building platforms from scratch strongly preferred. Trading, HFT, or fintech experience is a plus.
• Strong proficiency in Python and SQL; familiarity with high‑speed data protocols (FIX, ITCH, multicast) is advantageous.
• Experience with high‑throughput, event‑driven systems (e.g., Kafka, Spark/Flink) and stateful processing.
• Deep understanding of distributed systems, storage architectures, partitioning, ordering guarantees, and performance trade‑offs.
• Experience operating in on‑prem or self‑managed environments, including deployment automation and cluster operations.
• Familiarity with columnar storage (e.g., Parquet), data lake architectures, or time‑series databases.
• Proven ability to operate independently and deliver production‑grade systems end‑to‑end.

Nice to Have

• Experience with market data ingestion, exchange feeds, order book reconstruction, or pcap workflows.
• Background in low‑latency or real‑time trading systems.

Why Join Us?

• Foundational Impact: Shape the firm’s core data platform and influence the long‑term research and trading technology strategy.
• Flexible Working Options: Hybrid schedule based in Chicago.
• Startup Environment: Agile, entrepreneurial culture encouraging ownership and rapid decision‑making.
• Efficient Infrastructure: Proprietary low‑latency platform supporting systematic & discretionary trading.
• Comprehensive Health Coverage: Medical, dental, and vision insurance.
• 401(k) Retirement Plan: Supporting long‑term financial security.