M
Mastercard
Data Engineer
Skills
SQL QueriesData EngineeringE-commerce WebsitesDockerReactNodeTypescriptPythonJavaSAP HANASnowflakeGitData AnalysisMachine LearningAIKubernetesWorkdaySAP ERPTalendChange ManagementJavascriptAzure
2 months ago
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Important information
Contract type:
Permanent
Location:
Toronto, ON, Canada
Work mode:
Onsite
Published on:
13 August 2026
What they need
- 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- Strong problem-solving, communication, collaboration, analytical thinking, ownership mindset, and attention to detail
- Ability to debug, optimize code, automate routine tasks, and troubleshoot production application and data issues using a structured problem-solving approach
- Familiarity with ETL/ELT tools and data movement platforms such as Apache NiFi, Azure Data Factory, Pentaho, Talend, or similar technologies
- Strong SQL and programming skills, with experience in Python, Java, Node.js, or similar technologies
- Working knowledge of cloud infrastructure, cloud-native patterns, source control, Git, CI/CD, testing, deployment, monitoring, and production support
- Comfortable collaborating across technical and non-technical teams, communicating trade-offs, and contributing to practical delivery decisions
- Exposure to JavaScript/TypeScript, React, Angular, Docker, Kubernetes, or similar technologies
- Bachelor’s degree or equivalent experience in computer science, software engineering, data engineering, mathematics, quantitative science, or a related technical field
- Familiarity with full stack development concepts, including frontend frameworks, RESTful APIs, authentication, application security, backend services, integration patterns, and operational dashboards
- Understanding of data warehousing concepts, data lakes, data modeling, dimensional modeling, data integration, BI environments, and analytics/data processing engines
- Understanding of data quality, lineage, governance, privacy, security, responsible AI, MLOps, GenAIOps, deployment, monitoring, and reliable AI data flows
- Experience or familiarity with AI-ready data pipelines, ML/generative AI workloads, LLM APIs
- 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
- Interest in applying AI responsibly to improve data engineering productivity, automation, analytics, decision support, and customer-facing capabilities
- Understanding of database change management, deployment planning, migrations, upgrades, mitigation planning, and performance tuning across data pipelines, application services, APIs, and cloud resources
- Experience in banking, e-commerce, credit cards, payment processing, or high-growth fintech environments
- Exposure to both SaaS and premises-based architectures across enterprise data, application, and integration platforms
- Experience or familiarity with working in agile delivery environments and partnering with cross-functional teams to deliver production-ready solutions
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