Overview

Front Office Data Engineer

Responsibilities

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

Qualifications

• 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.
• 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.