Sr Lead Software Engineer - Quant, Python, KDB+

J.P. Morgan; Greater London; On-site; GBP 80,000 - 110,000; Full time

Job summary

A leading global financial services firm in London is seeking a Senior Lead Software Engineer to enhance and deliver cutting-edge technology products within the Equities Electronic Trading team. The role involves designing and implementing data pipelines, onboarding datasets, and building robust tools for quantitative research. The ideal candidate has strong expertise in Python and KDB/Q, along with a solid background in market data processing and automation methodologies. This position offers significant influence on technology and its application in trading systems.

Qualifications

• Design and implement front-office systems for quant trading.
• Deliver system design, application development, testing, and operational stability.
• Tackle design and functionality problems independently.
• Proficiency in automation and continuous delivery methods.

Responsibilities

• Build and support globally consistent data pipelines for research and execution systems.
• Onboard new datasets for use globally by trading teams.
• Design and build research infrastructure and analytics libraries.
• Influence decision-makers on leading-edge technologies.

Skills

Python

KDB/Q

Data pipelines

Automation

Market Data

Tools

Pandas

NumPy

Java

C++

Job description

Be an integral part of a technology team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Sr Lead Software Engineer at JPMorgan Chase within the Equities Electronic Trading team, you will play a crucial role in improving, developing, and delivering top-tier technology products in a secure, stable, and scalable manner. Your skills and contributions will have a substantial impact on the business, and your profound technical expertise and problem-solving methodologies will be utilized to address a wide range of challenges across various technologies and applications.

Job responsibilities

• Build and support fast, reliable, globally consistent data pipelines (data ingestion, cleaning, backfilling, storing) for the research and execution systems ensuring data integrity and low-latency access for research and trading.
• Work with the research and trading teams to onboard new datasets efficiently and consistently for use globally by the business.
• Design and build robust tools and frameworks to support quantitative research and production trading.
• Design, build and support research infrastructure (e.g. data access APIs, high performant and scalable simulation environments, feature and strategy signal stores)
• Build and support research and trading analytics libraries (e.g. markouts, strategy analytics)
• Serve as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
• Influence peers and project decision-makers to consider the use and application of leading-edge technologies

Required qualifications, capabilities, and skills

• Design and implementation of front-office systems for quant trading.
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Strong expertise in Python. Comfortable with scientific & dataset libraries such as pandas, numpy.
• Experience with KDB/Q
• Knowledge of data pipelines, market data processing and backtesting workflows.
• Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines
• Ability to tackle design and functionality problems independently with little to no oversight
• Proficiency in automation and continuous delivery methods

Preferred qualifications, skills and capabilities

• Strong knowledge and experience in FIX, Market Data, Analytics, OMS, and equities trading in global markets are assets
• Additional knowledge of Java / C++ is a strong plus.
• Practical cloud native experience is a plus.
• Practical cloud experience is a plus.