Data Engineer

Data Engineer - Python, Snowflake
Front-office domain must have

Build and enhance data ingestion and transformation pipelines for critical front-office and investment data sources, including near-real-time feeds where required.
• Design and develop database structures across multiple stages of refinement, from source-aligned data through curated, consumption-ready datasets.
• Work with data spanning core investment domains including positions and holdings, transactions, security and instrument master data, pricing and valuations, market data, reference data, accounts, portfolios, investment structures, and related risk and analytics data.
• Develop APIs and data-access patterns that allow applications and analytics workflows to efficiently consume curated datasets.
• Build intuitive React-based user interfaces that allow investment professionals and internal users to explore, validate, and interact with data.
• Partner with Portfolio Management, Trading, Risk, and Data teams to understand business workflows and translate them into well-designed technical solutions.
• Investigate and improve existing datasets and pipelines with focus on data quality and reconciliation, pipeline reliability and performance, query performance, data lineage and transparency, and usability for downstream consumers.
• Apply software engineering best practices including testing, code reviews, documentation, version control, and production validation.
• Use modern AI-assisted software development tools to accelerate engineering, testing, debugging, documentation, and analysis.
• Explore opportunities to make trusted investment data more accessible to AI assistants, agents, and other AI-enabled workflows.
• Participate in production support and help troubleshoot data or application issues when they arise.

Key responsibilties:

Experience using modern AI development tools such as Codex, Claude, Cursor, GitHub Copilot, or similar tools, with an interest in incorporating AI meaningfully into day-to-day software development.
• Strong programming skills in Python, with experience building production-quality data pipelines, services, or applications.
• Experience developing modern web applications using React and JavaScript/TypeScript.
• Strong SQL skills and practical experience designing, querying, and optimizing relational or analytical database structures.
• Experience building data pipelines involving ingestion, transformation, validation, and delivery of large or complex datasets.
• Experience developing or consuming REST and/or GraphQL APIs.
• Working knowledge of cloud-based data environments and modern data warehouses; Azure and Snowflake experience are strongly preferred.
• Strong understanding of the full data lifecycle: source -> ingestion -> transformation -> database / curated datasets -> API / application / analytics consumption.
• Demonstrated ability to troubleshoot data issues across multiple layers, including source data, transformations, databases, APIs, and user-facing applications.
• Experience working in an asset management, investment management, capital markets, or similarly data-intensive financial environment.
• Familiarity with the core data concepts that underpin front-office investment workflows, including positions / holdings, transactions, pricing, market data, security master, reference data, account and portfolio data, and risk or analytics data.
• Ability to understand how these data domains relate to one another and how they are consumed by Portfolio Managers, Traders, Risk professionals, and investment analytics users.
• Comfortable working in a fast-paced engineering environment with shared ownership of production