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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Big Data Engineer Lead - **Company:** Sp Global, Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $149,302.0 - $202,267.0 - **Contract:** Permanent contract - **Skills:** HTML, Java (Programming Language), JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Data Analysis, Architectural Patterns, Big Data, Data as a Services, Data Cleansing, Information Engineering, Cursor (Graphical User Interface Elements), Programming Tools, Distributed Computing Environment, Apache Hadoop, Python (Programming Language), Machine Learning, Natural Language Processing, Systems Development Life Cycle, Tensorflow, Standard Sql, Software Engineering, Unstructured Data, Virtual Machines, Virtualization Technology, Cloud Platform System, Feature Engineering, GitHub Copilot, Pytorch, ReactJS, Apache Spark, Spring-boot, Deep Learning, Software Application Programming, Generative AI, Git, Data Lakes, Scikit Learn, Kubernetes, Data Analytics, Data Management, Front End Software Development, Virtual Agents, Software Version Control, Docker - **Published:** September 10, 2026 - **Apply:** https://spgi.wd5.myworkdayjobs.com/SPGI_Careers/job/New-York-NY/Big-Data-Engineer-Lead_324302-1 ## About the Role * Experience building AI, machine learning, or data-driven solutions in cloud environments such as AWS. * Strong Python programming skills with experience using machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. * Hands-on experience developing applications with Java and Spring Boot. * Experience working with containerized and virtualized environments, including Docker, Kubernetes, and virtual machines. * Familiarity with modern AI-assisted development tools such as Cursor, Claude Code, GitHub Copilot, or similar platforms. * Experience using version control systems such as Git. * Strong knowledge of SQL and experience working with structured and unstructured datasets. * Understanding of machine learning concepts, including deep learning, natural language processing, and generative AI. * Experience building or working with AI agents, autonomous workflows, or agentic AI frameworks. Additional Preferred Qualifications: * Experience with AI frameworks such as LangChain, LangGraph, CrewAI, ADK, Strands, or similar technologies. * Experience with graph technologies and graph-based algorithms. * Familiarity with front-end technologies such as React, JavaScript, HTML, or comparable frameworks. * Experience in data engineering, data preparation, and feature engineering. * Experience working with data lakes, AI-ready data platforms, or large-scale data ecosystems. * Experience building machine learning solutions using distributed computing frameworks such as Apache Spark, Hadoop, or similar technologies. * Knowledge of modern AI-native software development lifecycle (SDLC) practices. ## Description Grade Level (for internal use): 12 The Team: You will be an expert contributor within the Ratings Organization's Data Services Team. Our team brings deep expertise across critical Ratings data domains, AI/ML and Generative AI solutions, technology platforms, and architectural patterns. Through collaboration, innovation, and knowledge sharing, we help drive a unified strategy and deliver scalable solutions that create business value. Data Services team members play a key role in providing technical leadership, fostering innovation, and delivering impactful solutions. This is a unique opportunity to contribute to and help shape the next generation of S&P Ratings' analytics platform. Responsibilities and Impact: * Partner with business stakeholders to gather requirements, define use cases, and plan solution delivery. * Collaborate with data scientists and software engineers to design, build, and deploy scalable machine learning and generative AI solutions. * Analyze large datasets and develop data-driven insights to improve business outcomes. * Build platforms, services, and tooling to optimize, evaluate, and fine-tune machine learning models for performance and scalability. * Research and apply emerging AI and machine learning technologies to enhance existing solutions and drive innovation. * Work closely with cross-functional teams to deliver high-quality, reliable solutions in a fast-paced environment. * Contribute to the development of best practices, technical standards, and reusable AI capabilities across the organization., At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets. S&P Global has a Securities Disclosure and Trading Policy ("the Policy") that seeks to mitigate conflicts of interest by monitoring and placing restrictions on personal securities holding and trading. The Policy is designed to promote compliance with global regulations. In some Divisions, pursuant to the Policy's requirements, candidates at S&P Global may be asked to disclose securities holdings. Some roles may include a trading prohibition and remediation of positions when there is an effective or potential conflict of interest. Employment at S&P Global is contingent upon compliance with the Policy. Recruitment Fraud Alert: If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, "pre-employment training" or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [The Resilience of the World Wide Web](https://www.wearedevelopers.com/videos/1281-the-resilience-of-the-world-wide-web) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? 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