Data Engineer II

Mastercard • Toronto, ON

2d • CA$91,000 - CA$140,000

About The Position

Ethoca, a Mastercard company, is seeking a Data Engineer with full stack engineering and AI enablement experience to help build scalable data, analytics, application, and AI-ready solutions across our on-premise and cloud technology landscape. This role is part of a small, agile, high-performing team focused on resilient, secure, maintainable, and intelligent platforms that support a high-growth fintech marketplace. The ideal candidate brings a strong foundation in data engineering, software engineering practices, and analytical problem solving. This role offers the opportunity to contribute across data pipelines, reporting, analytics, APIs, applications, and AI enablement capabilities while working with teams across Ethoca and Mastercard.

Requirements

• 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.
• 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.

Nice To Haves

• 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.
• Understanding of database change management, deployment planning, migrations, upgrades, mitigation planning, and performance tuning across data pipelines, application services, APIs, and cloud resources.
• Experience or familiarity with working in agile delivery environments and partnering with cross-functional teams to deliver production-ready solutions.

Responsibilities

• 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.

Benefits

• Competitive pay based on location, experience and other qualifications for the role
• May be eligible to participate in a discretionary annual incentive program

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Associate degree