Investment Data Engineer

Investment Data Engineer

This position is suited to an experienced data engineer who is passionate about building reliable, scalable data infrastructure within an investment environment. You will collaborate across investment, operations, and technology functions to develop high-quality data pipelines, strengthen data capabilities, and deliver analytics solutions that enable informed business and investment decisions.
The successful candidate will have a strong engineering foundation and extensive experience with modern data engineering principles, architecture, and tooling. Experience within asset management, investment management, hedge funds, wealth management, or broader financial services is highly valued.

Key Responsibilities

• Architect, develop, and maintain scalable data pipelines supporting investment and financial datasets.
• Build and enhance ETL and ELT workflows that ingest, transform, standardize, and validate data from a broad range of internal and external sources.
• Work with portfolio, market, benchmark, reference, transaction, and other investment datasets to maintain accuracy, consistency, and usability.
• Connect and integrate data from investment management systems, custodians, fund administrators, and third-party financial data providers.
• Design and maintain data models that support downstream reporting, analytics, research, and operational requirements.
• Develop clean, efficient, and maintainable Python applications for data processing, automation, and pipeline development.
• Build, orchestrate, monitor, and improve data workflows using Dagster.
• Engage with business and investment stakeholders to understand data requirements and translate them into dependable technical solutions.
• Incorporate data governance, lineage, observability, validation, and quality controls throughout the data lifecycle.
• Partner with software engineers, data analysts, researchers, and investment professionals to improve data availability, usability, and platform performance.
• Investigate and resolve production data issues while identifying opportunities to improve the stability, scalability, and resilience of the underlying data platform.
• Contribute to the development of engineering standards, reusable frameworks, and best practices across the investment data environment.

Required Skills & Experience

• Proven experience working as a Data Engineer within a modern data engineering environment.
• Advanced proficiency in Python, particularly for data engineering, automation, and large-scale data processing.
• Practical experience using Dagster or comparable orchestration frameworks to manage complex data workflows.
• Strong SQL capabilities and experience working with relational databases and cloud-based data platforms.
• Demonstrated ability to design, implement, and support enterprise-scale ETL/ELT pipelines.
• Strong knowledge of data modelling, data integration, data architecture, and data quality principles.
• Experience with cloud technologies such as AWS, Azure, or GCP is beneficial.
• Familiarity with contemporary data warehouse and lakehouse technologies is desirable.
• Strong analytical and troubleshooting abilities, combined with a high level of attention to detail.
• Ability to understand complex technical problems, identify root causes, and develop practical, sustainable solutions.

Desirable Investment Experience

• Professional experience within investment management, asset management, hedge funds, wealth management, or financial services.
• Familiarity with core investment datasets, including:
  
  • Portfolio holdings and positions
  • Security master and reference data
  • Market and pricing data
  • Performance and attribution data
  • Valuation data
  • Benchmark data
  • Corporate actions
• Experience connecting investment systems with external financial data providers and market data platforms.
• Understanding of investment operations and the movement of data through front-, middle-, and back-office processes.
• Exposure to investment workflows such as portfolio management, trading, performance measurement, risk, and reporting.

Personal Attributes

• Strong engineering orientation with a focus on building dependable, scalable, and maintainable data solutions.
• Clear and confident communicator who can work effectively with both technical teams and business stakeholders.
• Proactive, self-directed, and naturally curious, with a structured approach to solving complex problems.
• Comfortable operating in a dynamic, collaborative environment where priorities can evolve.
• High standards for data accuracy, system reliability, and engineering quality.
• Strong sense of ownership, with a willingness to take responsibility for solutions from initial design through production support and continuous improvement.