Job Description

Job Summary: As a Data Engineer at S&P Global, you will utilize your extensive technical skills to architect, build, and maintain our evolving data infrastructure, which is essential for supporting our advanced analytics and machine learning initiatives. You will work closely with various stakeholders to acquire, process, and refine vast datasets, focusing on creating scalable and optimized data pipelines. Your work will be pivotal for enabling data scientists to extract meaningful insights and develop AI-driven solutions that serve various business purposes, from internal decision-making to product development. Responsibilities: To collaborate with stakeholders, including data scientists, analysts, and other engineers, to understand and refine requirements related to data processing and transformation needs. To design, construct, install, and maintain large-scale processing systems and other infrastructure. To build high-performance algorithms, prototypes, and conceptual models and enable the efficient retrieval and analysis of data. To implement ETL processes to acquire, validate, and process incoming data from diverse sources. To ensure data architecture and model adhere to compliance, privacy, and security standards. To work in conjunction with data scientists to optimize data science and machine learning algorithms and models. To provide technical expertise in the resolution of data-related issues, including data quality, data lineage, and data processing errors. To manage the deployment of analytics solutions into production and maintain them. To maintain high-quality processes and deliver projects in collaborative Agile team environments. Requirements: 7+ years of programming experience particularly in Python, R, Java or C#. 4+ years of experience working with SQL or NoSQL databases. 1+ years of experience working with Bigdata processing engines. University degree in Computer Science, Engineering, Mathematics, or related disciplines. Strong understanding of big data technologies such as Hadoop, distributed data processing framework, or message broker systems. Demonstrated ability to design and implement end-to-end scalable and performant data pipelines. Experience with workflow management platforms like Airflow. Strong analytical and problem-solving skills. Ability to collaborate and communicate effectively with both technical and non-technical stakeholders Experience building solutions and working in the Agile working environment Experience working with git or other source control tools Nice to have: Experience working with Oil, gas, and energy markets. Familiarity with BI Visualization applications (e.g. Tableau, Power BI, Spotfire). Understanding of cloud-based services, preferably AWS. Experience working with Unified analytics platforms like Databricks. Experience in managing and deploying containerized applications using Docker, Kubernetes, or similar technologies. Machine learning/Data Science experience In this role, Data Engineers are expected to work collaboratively and efficiently, ensuring that the data needs of the team are met and maintaining the highest standards of data management and deployment practices. By joining our team, you will play a critical role in the advancement of our data capabilities and contribute directly to the success of our business initiatives.