Data Engineer

Crisil; Maharashtra; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Crisil is seeking a Data Engineer - Python, Snowflake to build and optimize data ingestion, transformation, and storage for front-office and investment datasets. You will design scalable pipelines, implement APIs, and help enable AI-enabled workflows across teams.

You will collaborate with Portfolio Management, Trading, Risk, and Data teams to deliver reliable data solutions, while creating UI components in React for data exploration and validation.

Qualifications

• Strong experience in building data ingestion/ transformation pipelines.
• Experience with front-office or investment data is a plus.
• Familiarity with data quality, lineage, and performance.
• Ability to design APIs and data-access patterns.

Responsibilities

• Build and enhance data ingestion and transformation pipelines for front-office and investment data sources, including near-real-time feeds.
• Design and develop database structures across data refinement stages.
• Work with data spanning investments, pricing, risk, and reference data.
• Develop APIs and data-access patterns for consumption by apps and analytics.
• Create React-based UIs for investment professionals to explore and validate data.
• Collaborate with Portfolio Management, Trading, Risk, and Data teams.
• Ensure data quality, reliability, and performance; improve pipelines and lineage.
• Apply software engineering best practices including testing and version control.

Skills

Python

SQL

ETL

Data modeling

Data warehousing

API design

Cloud data platforms

Tools

Snowflake

React

Job description

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.