Senior Data Engineer

Our Purpose Mastercard powers economies and empowers people in 200 countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Engineer Who is Mastercard? We are the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee has the opportunity to be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities. What is the AI Ops team? The AI Ops team uses AI, machine learning, and data science techniques to detect anomalies, predict impact, and initiate remediation. The team creates insights—in the form of dashboards, reports, and alerts—from operations data and helps stakeholders make data-driven decisions for planning, troubleshooting, or monitoring application health. Overview The Senior Data Engineer for AI Ops designs, builds, and operates analytics and data engineering solutions that provide operational visibility, insight, and proactive intelligence across enterprise infrastructure, applications, and services. This role focuses on integrating data from diverse operational sources, enabling high-quality dashboards and metrics, and supporting AI-driven operational use cases that improve reliability, performance, and decision-making. Key Responsibilities • Design, build, and maintain scalable ETL and data pipelines using tools such as Alteryx, Apache NiFi, Apache Airflow, or similar technologies. • Integrate and normalize data from multiple operational sources, including monitoring platforms, incident and ticketing systems, SLA/SLO reports, surveys, and enterprise data stores. • Develop and maintain analytics and visualizations using tools such as Power BI, Grafana, and related platforms to deliver operational, management, and executive-level insights. • Ensure data quality, consistency, and reliability through validation, monitoring, and continuous improvement of data pipelines. • Translate operational and business questions into measurable indicators, metrics, and dashboards that clearly communicate current state, risks, trends, and improvement opportunities. • Support incident analysis, root cause investigations, and operational reviews through data-driven insights. • Partner with AI Ops, SRE, infrastructure, and software engineering teams to operationalize analytics and enable advanced automation and AI/ML use cases. • Create and maintain technical documentation, operational procedures, and best practices to support governance, scalability, and reuse. All About You: • Analytical, investigative and problem-solving skills • Strategic thinker with ability to derive and translate data analytics to meet business goals • Sound written and verbal communication skills • Project management skills, highly organized with strong attention to detail • Must be able to work independently in developing and mapping out solutions • Must be able to work in a fast paced and dynamic environment, handle multiple tasks, consistently meet established deadlines, and deliver exceptional results • Strong experience in data engineering and analytics, working with structured, semi-structured, and unstructured data. • Hands-on experience with ETL and workflow orchestration tools such as Alteryx, NiFi, Airflow, or equivalent. • Proven ability to build dashboards and visualizations using Power BI, Grafana, or similar tools. • Solid under ... (truncated, view full listing at source)