- CRISIL is a leading, agile and innovative global analytics company driven by its mission of making markets function better.
- It has delivered independent opinions, actionable insights, and efficient solutions to over 100,000 customers through businesses that operate from India, the US, the UK, Argentina, Colombia, Poland, China, Hong Kong and Singapore.
- It is majority owned by S&P Global Inc, a leading provider of transparent and independent ratings, benchmarks, analytics and data to the capital and commodity markets worldwide.
Position Summary
This is a hands-on, mid-level engineering role on a globally distributed Data Engineering team. You
will help build and enhance data products used by Portfolio Management, Trading, Risk Analytics, and other investment teams. The work spans data pipelines, database development, APIs, and React- based user experiences, with an emphasis on making complex financial data accurate, performant, intuitive, and easy for end users and AI-enabled workflows to consume.
This role is well suited to an engineer with roughly 6-8 years of professional experience who has
developed strong full-stack fundamentals and is ready to take meaningful ownership of features and data products while continuing to grow technically and deepen their understanding of investment data.
What You'll Do
- 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.
What we're looking for (Must-haves)
- 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.
- 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 systems.
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.
Success looks like
- Develop a strong understanding of the team's core front-office datasets, their sources, and how they are consumed by Portfolio Management, Trading, and Risk users.
- Become productive across the existing data pipeline, database, API, and front-end codebase.
- Demonstrate an ability to trace data end-to-end and troubleshoot issues spanning ingestion, transformation, storage, APIs, and application consumption.
- Independently deliver multiple production-quality features spanning data pipelines, databases, APIs, and/or user-facing applications.
- Build a strong working knowledge of the key investment data domains and their relationships.
- Improve the reliability, performance, and usability of datasets consumed by front-office investment teams.
- Contribute to curated data products that provide consistent, trusted views of positions, transactions, securities, pricing, market data, accounts, and related analytics.
- Help expand the ways users and AI-enabled applications can interact with trusted enterprise
- Become a dependable engineering partner to both Data Engineering teammates and front-office