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.

Work arrangement
No