Data Engineering - Full Stack Engineer

ROLE OVERVIEW

We are looking for an AI-forward Full Stack Engineer who is comfortable working across the data lifecycle -from source ingestion and transformation through database design, APIs, and modern front-end experiences. The strongest candidates will bring experience working with investment-management data and understand how core data domains such as positions, transactions, pricing, market data, security master, reference data, account data, and related investment data fit together to support front-office use cases

KEY RESPONSIBILITIES

• 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, and 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.

REQUIRED SKILLS & EXPERIENCE

• Approximately 6-8 years of professional software engineering experience, with meaningful hands-on experience across both backend/data engineering and front-end development.
• 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.
• Experience with Azure, Snowflake, and Azure Kubernetes Service (AKS) is required.
• 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 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 systems.
• Domain knowledge in front-office workflows, including risk, trading, and positions. Familiarity with accounting systems is mandatory

Nice-to-haves

• Experience supporting Portfolio Management, Trading, Risk, or other front-office investment
• workflows directly.
• Experience with private markets, alternatives, or Private Equity data.
• Experience with Snowflake performance optimization and data modeling.
• Experience with near-real-time or event-driven financial data.
• Familiarity with Spark or other distributed data-processing frameworks.
• Experience developing semantic or analytics-ready datasets for tools such as Tableau, Power BI,
• Sigma, or Pyramid.
• Exposure to AI/LLM application development, including retrieval, tool use, agents, structured outputs, or natural-language interfaces over enterprise data.
• Java experience in addition to Python.