Front Office Data Engineer

JOB AND PERSON SPECIFICATION

JOB TITLE:

Front Office Data Engineer

LOCATION:

Bengaluru

REPORTS TO:

Head of Front Office Data and

Analytics Engineering

REASON FOR HIRE:

Replacement

JOB SPECIFICATION

COMPANY/DEPARTMENTAL OVERVIEW

The Firm:     

Brevan Howard Investment Management is one of the leading absolute return/hedge fund managers, overseeing assets on behalf of institutional investors from around the world, including pension funds, endowments, insurance companies, government agencies, private banks, and funds of funds.

Brevan Howard was founded in 2002 and launched its flagship global macro strategy in April 2003. The firm currently manages over $34bn and engages predominantly in discretionary directional and relative value trading in fixed income, FX markets, and equities. BH Digital, a division within Brevan Howard that manages crypto and digital asset strategies, was launched in 2022.

The firm currently employs over 1,050 personnel worldwide, including over 400 investment professionals. This global presence gives Brevan Howard the ability to identify and source attractive investment opportunities, as well as investment management talent wherever they may be. Brevan Howard has won several industry awards for excellence in risk management, operational robustness, and investment performance.

The firm’s main hubs are in London, Jersey, Geneva, New York, Austin, Hong Kong, Singapore, Abu Dhabi and Bengaluru.

About the Role:

We are seeking an experienced Data Engineer to join our Front Office Data and Analytics Engineering team in Bengaluru.

In this role, you will work closely with the wider Data and Analytics engineering team and Front Office Quants to design, build and support the data and analytics infrastructure that underpins research, trading, and portfolio decision-making across the firm.

This is a hands-on engineering role where you will take ownership of solutions from design through to production support. You will build and maintain scalable data platforms, pipelines and services that enable quantitative research and investment workflows across the firm, ensuring high-quality, reliable and timely data is available to support investment decision-making. You will be expected to operate with a self-starter mindset, thrive in a fast-paced, collaborative environment and contribute to the continuous evolution of the firm’s data and analytics capabilities.

MAIN DUTIES/RESPONSIBILITIES OF THE ROLE: 

Essential Responsibilities:

• Help design, build and maintain data platforms, pipelines and services that deliver high-quality, investment-enabling data across the firm.
• Work closely with Front Office Quantitative Researchers and the wider Data & Analytics Engineering team to understand data requirements and deliver robust, scalable solutions.
• Ingest, transform and serve large-scale financial datasets across multiple asset classes using Python, Snowflake and NoSQL databases (e.g. MongoDB).
• Ensure high data quality is delivered to the front office, introducing validation pipelines and dashboards for use by trading.
• Contribute to the design and evolution of the firm's data architecture, supporting pricing, risk and analytics capabilities.
• Take ownership of solutions throughout their lifecycle, from design and implementation through testing, deployment and production support.
• Provide first-line production support, including troubleshooting data issues, monitoring pipeline health and responding quickly to business-critical incidents.

PERSON SPECIFICATION

   WORK EXPERIENCE/BACKGROUND:

Essential

Desirable

• 5+ years of professional experience in data engineering or software engineering, ideally within a buy-side, sell-side or financial services environment.
• Strong expertise in Python with solid software engineering practices, including version control, testing and CI/CD.
• Proven experience designing, building and supporting scalable data pipelines and platforms in cloud-native environments (preferably AWS).
•  Experience with Docker and containerised deployments.
• Strong knowledge of Snowflake and NoSQL databases, particularly MongoDB.
• Good understanding of financial markets and financial instruments.
• Excellent problem-solving and analytical skills with a proactive, ownership-driven mindset.
• Ability to work independently and collaborate effectively with Quantitative Researchers and engineering teams.
• Strong communication skills with the ability to translate business requirements into technical solutions.
• Willingness to participate in on-call or production support rotations.

• Experience working with market data providers such as Bloomberg, Refinitiv or ICE.
• Experience with orchestration frameworks such as Airflow, Prefect or Dagster.
• Experience building internal tools or dashboards using Dash, Streamlit or similar frameworks.
• Experience with GoldenSource or other enterprise data management platforms.
• Experience with event-driven or streaming technologies such as Kafka.
• Experience supporting quantitative research or investment workflows within a front office environment.
• Experience developing low-latency or high-performance data platforms.
• Experience contributing to the design of enterprise data architecture.

TECHNICAL/BUSINESS SKILLS & KNOWLEDGE:

Essential

Desirable

• Strong understanding of data engineering principles, including data modelling, governance and lifecycle management.
• Knowledge of software engineering best practices, including clean code, testing, version control and CI/CD.
• Good understanding of cloud-native architectures and distributed systems.
• Strong understanding of financial markets, financial instruments and investment data.
• Ability to analyse complex business problems and translate them into scalable technical solutions.
• Strong stakeholder management and communication skills, with the ability to work effectively with Quantitative Researchers and engineering teams.
• A collaborative approach with a strong sense of ownership and accountability.

• Understanding of market data, pricing and reference data concepts.
• Knowledge of quantitative research and investment workflows.
• Familiarity with enterprise data management and governance frameworks.
• Awareness of modern data platform architectures and event-driven systems.
• Understanding of DevOps, observability and production support best practices.