Top Benefits
Gym membership
Pension plan
Share purchase options
About the role
Who you are
- You are a curious and collaborative problem-solver who enjoys working with data and technology to drive impact
- You have a solid foundation in data engineering and are eager to grow your skills in cybersecurity and analytics
- You communicate clearly and work well in a team environment, sharing knowledge and learning from others
- Proficiency in SQL and Python for data manipulation and scripting
- Experience with big data tools such as Spark, Hive, or Kafka
- Familiarity with ETL tools and orchestration frameworks like Airflow or NiFi
- Exposure to cloud platforms (preferably AWS) and data lake architectures
- Hands-on experience with Databricks, including Medallion Architecture and Delta tables
- Understanding of graph databases and their application in cybersecurity or fraud analytics
- Knowledge of CI/CD practices and infrastructure-as-code tools like GitHub Actions or Terraform is a plus
- Basic knowledge of Linux/Unix systems and shell scripting
- Interest or experience in cybersecurity, fraud detection, or digital payments is a plus
- Higher-education degree where you can demonstrate background knowledge of data, coding, and/or cyber (e.g., Mathematics, Statistics, Physics, Engineering, Cybersecurity, Computer Science), or equivalent work experience in which you acquired this background knowledge
What the job involves
- As a Data Engineer in the Cyber Analytics team at Mastercard, you will play a vital role in building and maintaining the data infrastructure that supports our mission to detect and mitigate cyber vulnerabilities across the Mastercard ecosystem. You’ll work closely with data scientists, machine learning engineers, and software developers to ensure secure, scalable, and high-quality data pipelines
- Collaborate with cross-functional teams to design and implement scalable data pipelines and workflows
- Support the development of a modern data platform using cloud-native and big data technologies
- Ensure data quality and integrity through testing, validation, and monitoring
- Assist in the integration of data sources and the delivery of data to analytics and ML teams
- Contribute to the deployment and maintenance of production-grade data solutions in partnership with software engineering teams
Benefits
- Gym membership
- Pension plan
- Share purchase options
- Extra holiday purchase optional
- 25 days holiday (excl. bank holidays)
- Work from home opportunities
- Health insurance
About Mastercard
IT Services and IT Consulting
10,000+
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building 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.
Top Benefits
Gym membership
Pension plan
Share purchase options
About the role
Who you are
- You are a curious and collaborative problem-solver who enjoys working with data and technology to drive impact
- You have a solid foundation in data engineering and are eager to grow your skills in cybersecurity and analytics
- You communicate clearly and work well in a team environment, sharing knowledge and learning from others
- Proficiency in SQL and Python for data manipulation and scripting
- Experience with big data tools such as Spark, Hive, or Kafka
- Familiarity with ETL tools and orchestration frameworks like Airflow or NiFi
- Exposure to cloud platforms (preferably AWS) and data lake architectures
- Hands-on experience with Databricks, including Medallion Architecture and Delta tables
- Understanding of graph databases and their application in cybersecurity or fraud analytics
- Knowledge of CI/CD practices and infrastructure-as-code tools like GitHub Actions or Terraform is a plus
- Basic knowledge of Linux/Unix systems and shell scripting
- Interest or experience in cybersecurity, fraud detection, or digital payments is a plus
- Higher-education degree where you can demonstrate background knowledge of data, coding, and/or cyber (e.g., Mathematics, Statistics, Physics, Engineering, Cybersecurity, Computer Science), or equivalent work experience in which you acquired this background knowledge
What the job involves
- As a Data Engineer in the Cyber Analytics team at Mastercard, you will play a vital role in building and maintaining the data infrastructure that supports our mission to detect and mitigate cyber vulnerabilities across the Mastercard ecosystem. You’ll work closely with data scientists, machine learning engineers, and software developers to ensure secure, scalable, and high-quality data pipelines
- Collaborate with cross-functional teams to design and implement scalable data pipelines and workflows
- Support the development of a modern data platform using cloud-native and big data technologies
- Ensure data quality and integrity through testing, validation, and monitoring
- Assist in the integration of data sources and the delivery of data to analytics and ML teams
- Contribute to the deployment and maintenance of production-grade data solutions in partnership with software engineering teams
Benefits
- Gym membership
- Pension plan
- Share purchase options
- Extra holiday purchase optional
- 25 days holiday (excl. bank holidays)
- Work from home opportunities
- Health insurance
About Mastercard
IT Services and IT Consulting
10,000+
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building 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.