Front Office Data Engineer

Mancer Consulting Services; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Investment Management is seeking an experienced Data Engineer in Bengaluru to join the Front Office Data and Analytics Engineering team. You will design, build and support scalable data platforms and pipelines that underpin quantitative research, trading and portfolio decision-making across the firm.

You will work closely with Front Office Quants and the analytics team, ingesting and transforming large-scale datasets, ensuring high data quality and availability for investment decisions in a

Qualifications

• 5+ years of professional experience in data engineering or software engineering.
• Strong Python programming with good testing/CI/CD practices.
• Experience building scalable data pipelines and platforms in cloud-native environments (AWS).
• Knowledge of Snowflake and NoSQL databases (MongoDB).

Responsibilities

• Design, build and maintain data platforms, pipelines and services.
• Collaborate with Front Office Quants and Data Analytics Engineering to meet data requirements.
• Ingest, transform and serve large financial datasets across asset classes using Python and Snowflake.
• Ensure high data quality with validation pipelines and dashboards for trading use.
• Contribute to data architecture design supporting pricing, risk and analytics.
• Own solutions from design through testing, deployment and production support.
• Provide first-line production support and monitor pipeline health.

Skills

Python

Snowflake

MongoDB

Docker

CI/CD

AWS

Tools

Airflow

Dagster

Prefect

Job description

The Firm:

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.

We 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 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 us the ability to identify and source attractive investment opportunities, as well as investment management talent wherever they may be. We 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 firms 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

• 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.

Desirable

• 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

• 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.

Desirable

• 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.