Senior Data Engineer

Mastercard Payment Services Denmark A/S; København; On-site; DKK 900,000 - 1,100,000; Full time

Job summary

Mastercard Payment Services Denmark A/S in Ballerup is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines, ETL/ELT processes, and data products for Open Finance analytics.

You will collaborate with Product and Engineering teams to ensure analytics infrastructure aligns with Open Finance strategy, while maintaining data quality, security, and regulatory readiness across platforms.

Qualifications

• Advanced SQL, Python, Spark and data engineering skills.
• Experience with Databricks, dbt Cloud and modern data platforms.
• Knowledge of data governance, security, and regulatory controls.
• Experience with AI/ML data pipelines and analytics initiatives.

Responsibilities

• Design, build and maintain scalable data pipelines and ETL/ELT processes.
• Partner with Product, Business and Engineering teams to align analytics infra with strategy.
• Define data capture, instrumentation, and measurement for new features.
• Ensure data quality, security, reliability and regulatory readiness.
• Contribute to data architecture, platform design and reusable patterns.
• Mentor junior engineers and support production runbooks and incident management.

Skills

SQL

Python

Spark

Kafka

Databricks

Data modeling

Distributed data processing

Regulatory compliance

Education

Bachelor's or Master’s in CS/Engineering/Data Science

Tools

Tableau

PowerBI

Databricks SQL

DBT Cloud

Snowflake

Job description

Placering
Ballerup

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
Overview

Role

• Design, build, and maintain scalable data pipelines, ETL/ELT processes, workflow orchestration, and data products that support Open Finance analytics, reporting, operational, and AI use cases.
• Partner with Product, Business, and Engineering teams to ensure analytics infrastructure is aligned with Open Finance product strategy, business priorities, and long-term scalability needs.
• Collaborate during the discovery and design phases of new features and products to define data capture, instrumentation, and measurement requirements, enabling reliable post-launch analysis.
• Ensure data solutions are designed for high standards of data quality, security, reliability, performance, and regulatory readiness.
• Contribute to data architecture, platform design, engineering standards, and reusable patterns that support Open Finance growth and long-term scalability.
• Design and support ETL/ELT pipelines, data integration processes, and workflow orchestration required for reliable data movement and transformation.
• Participate in technical design reviews and code reviews to uphold engineering quality, maintainability, and best practices.
• Create and maintain technical documentation, design specifications, implementation plans, and operational runbooks.
• Support deployment, monitoring, incident management, and operational management of production data pipelines and data platforms.
• Perform complex troubleshooting, root cause analysis, performance tuning, and problem resolution across production data solutions.
• Mentor junior engineers, share knowledge, and contribute to continuous improvement of engineering practices across the team.

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

ALL ABOUT YOU

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• Strong familiarity with BI and analytics tools such as Tableau, PowerBI, Databricks SQL and Dashboarding capabilities, semantic data models/layers, and self-service reporting frameworks to support analytics, operational reporting, and business intelligence use cases.
• Advanced technical expertise in one or more of the following SQL, Python, Spark, Kafka, and Databricks, with the ability to design, build, and optimize scalable data engineering solutions.
• Deep experience with enterprise data platforms, cloud-based data platforms, lakehouse architectures, modern data warehousing solutions, and RDBMS technologies, including Snowflake and SQL-based environments.
• Experience designing and supporting ETL/ELT pipelines, workflow orchestration, and data integration frameworks.
• Experience with dbt/DBT Cloud for SQL-based data transformation, reusable data modeling, data quality testing, documentation, and creation of trusted datasets for analytics and business intelligence use cases.
• Strong knowledge of data modeling, data integration, distributed data processing, and scalable data architecture patterns.
• Knowledge of data governance, security, privacy, and regulatory compliance controls.
• Databricks certifications such as Databricks Certified Data Engineer Associate, Databricks Certified Data Engineer Professional, or equivalent cloud data engineering certifications are preferred.
• Experience supporting AI/ML and advanced analytics initiatives.

Corporate Security Responsibility

Open Finance Focus Areas

• Open Banking and Open Finance data platforms
• Data quality and governance initiatives
• Product performance and operational analytics
• API and event-driven data architectures
• AI and advanced analytics enablement