Java fejlesztő, CDP API

MSCI is a leading provider of decision-support tools and services for the global investment community. With over 50 years of expertise in research, data, and technology, we power better investment decisions through our indexes, analytics, and ESG and climate products. Our Data Operations & Technology function collects, validates, quality-assures, and delivers the data that underpins every MSCI product — spanning ESG, climate, sustainability, corporate governance, geospatial, and private-assets data across thousands of companies and assets worldwide.

MSCI is transforming its data operations through an agentic AI operating model — replacing manual, human-driven workflows with intelligent, autonomous agents that collect, validate, and quality-assure data at scale. As a Data Engineer, you build the automation that makes this possible: you design, engineer, and deploy the AI agents, orchestration, and data pipelines that operations teams run every day, taking each solution from concept through to a reliable, production-grade system. You architect, engineer, integrate, and deploy large-scale market-data solutions across MSCI’s multi-asset-class products — Analytics, Sustainability, Climate, Index, and Private Assets — collaborating with product, data-content, and infrastructure teams in a fast-paced Agile environment.

The CDP API team is building the Centralized Data Platform Data Marketplace — the API-first distribution layer that turns MSCI's data into a modern, self-service product. Our mission is to make MSCI's datasets discoverable, subscribable, and consumable programmatically, so a quant developer, a portfolio manager working in Excel, or an AI agent can find the right data and start using it in minutes rather than weeks.

• Design, build, and deploy production-grade agentic AI and automation solutions that collect, validate, and quality-assure data at scale.

• Engineer multi-agent orchestrations, data pipelines, MCP integrations, and API connections that power autonomous, end-to-end data workflows.

• Take each solution from design through to production — writing clean, reliable, well-tested code and engineering for scale, resilience, and maintainability.

• Architect and develop secure, high-performance, scalable, and maintainable applications, applying modern architectural patterns and best practices for design, coding, and automated test coverage.

• Automate the operation and management of MSCI’s market-data and terms-and-conditions databases, engineering for availability, low latency, scalability, and fault tolerance.

• Work in a DevOps model — building CI/CD pipelines, automated testing frameworks, and production monitoring, telemetry, and feedback loops.

• Build and maintain the platforms, frameworks, and tools that operations teams depend on, and make them easy to run and troubleshoot.

• Monitor deployed solutions in production, diagnose and resolve issues, and deliver enhancements as data sources, methodologies, and processes evolve.

• Establish reusable components, patterns, and engineering standards that make every subsequent automation faster to build.

• 2-4 years of experience in roles that combine technology implementation with business/operational problem-solving — solutions engineering, forward deployed engineering, technical consulting, implementation engineering, or similar hybrid roles.

• Bachelor’s degree in Computer Science, Data Science, Financial or Quantitative Engineering, or a related field — or equivalent practical experience.

• Strong Python engineering skills, with SQL and PL/SQL for data work and day-to-day Linux/Unix and Git; JavaScript/TypeScript a plus. You write and debug clean, production-quality code.

• Data pipeline, orchestration, and database expertise — workflow engines (Airflow, Prefect), ETL/ELT, SQL and relational databases (Oracle, SQL Server, Azure SQL, Postgres), and big-data/cloud storage (ADLS, Snowflake).

• Experience building market-data systems in a multi-asset-class environment — reference data, corporate actions, benchmark/index, and pricing; familiarity with major data vendors (LSEG/Refinitiv, Bloomberg, S&P) and with financial, ESG/sustainability, or capital-markets data is an advantage.

• Comfortable delivering production-quality software with AI coding assistants (Claude Code, Cursor, GitHub Copilot, or Codex) and working in Agile/DevOps teams with CI/CD and automated testing; familiarity with MCP or reusable Skills is a plus.

• Hands-on experience building AI/ML-powered solutions — agent frameworks (LangGraph, CrewAI, or AutoGen), LLM APIs (Anthropic Claude, OpenAI), prompt engineering, RAG, and evaluation — and deploying and monitoring agentic systems at scale, are strong advantages.

• Demonstrated success shipping products quickly inside a tightly governed enterprise — meeting rigorous security, compliance, and change-control standards without sacrificing delivery speed.

• Systems thinking and strong communication — you design coherent end-to-end systems and explain technical decisions clearly to both engineers and non-technical stakeholders.