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, straightforward, 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. Role Contribute to the development and support of ETL/ELT, data movement, streaming and non-streaming data solutions, data warehousing, reporting, analytics, and BI capabilities. Help design, build, and support full stack solutions, including frontend experiences, backend services, APIs, reusable data services, operational dashboards, and enterprise integrations. Support the development of AI-ready data pipelines and intelligent application features for analytics, machine learning, generative AI, semantic search, and RAG use cases. Work with SAP HANA and Snowflake data environments with focus on configuration, data movement, security, reliability, performance, governance, and production readiness. Assist with debugging, optimization, automation, and troubleshooting of data, application, API, and cloud/on-premise issues. Contribute to CI/CD, testing, deployment, migration activities while helping minimize service impacts. Support performance tuning across data pipelines, queries, application services, APIs, and cloud or on-premise resources. Partner with architects, analysts, data engineers, application teams, and business stakeholders to deliver agile, data-driven, AI-enabled solutions. All About You Bachelor’s degree or equivalent experience in computer science, software engineering, data engineering, mathematics, quantitative science, or a related technical field. Strong SQL and programming skills, with experience in Python, Java, Node.Js,



or similar technologies. Understanding of data warehousing concepts, data lakes, data modeling, dimensional modeling, data integration, BI environments, and analytics/data processing engines. Familiarity with ETL/ELT tools and data movement platforms such as Apache NiFi, Azure Data Factory, Pentaho, Talend, or similar technologies. Working knowledge of cloud infrastructure, cloud-native patterns, source control, Git, CI/CD, testing, deployment, monitoring, and production support. Familiarity with full stack development concepts, including frontend frameworks, RESTful APIs, authentication, application security, backend services, integration patterns, and operational dashboards. Exposure to JavaScript/TypeScript, React, Angular, Docker, Kubernetes, or similar technologies. Ability to debug, optimize code, automate routine tasks, and troubleshoot production application and data issues using a structured problem-solving approach. Comfortable collaborating across technical and non-technical teams, communicating trade-offs, and contributing to practical delivery decisions. Strong problem-solving, communication, collaboration, analytical thinking, ownership mindset, and attention to detail. AI, Data Governance, and Production Readiness Experience or familiarity with AI-ready data pipelines, ML/generative AI workloads, LLM APIs. Understanding of data quality, lineage, governance, privacy, security, responsible AI, MLOps, GenAIOps, deployment, monitoring, and reliable AI data flows. Interest in applying AI responsibly to improve data engineering productivity, automation, analytics, decision support, and customer-facing capabilities. Ability to support reliable, scalable, secure, and well-governed data pipelines across structured and unstructured data use cases. Awareness of production readiness practices, including monitoring, operational reliability, testing, deployment controls, and supportability. Preferred Experience Experience in banking, e-commerce, credit cards,

📌 Data Engineer Ii (Toronto)
🏢 Mastercard
📍 Toronto