Lead Data Engineer
MastercardWarsaw, PolandPosted 22 March 2026
Job Description
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
Lead Data Engineer
Mastercard is seeking a Lead Data Engineer to drive the design, development, and optimization of scalable data solutions that power analytics, experimentation, and decision-making across the organization. In this role, you will serve as both a technical expert and a team leader—overseeing data pipeline architecture, ensuring data quality and reliability, and guiding engineers in best practices. You will collaborate closely with cross-functional partners to enable high-impact data products while shaping the long-term vision for data engineering capabilities.
Role:
* Lead the design, development, and maintenance of scalable, reliable data pipelines and data processing frameworks supporting a variety of business and product use cases.
* Ensure data quality, integrity, and readiness by establishing and maintaining standards, validation processes, and monitoring frameworks.
* Mentor and guide a team of data engineers, fostering strong technical craftsmanship, collaboration, and continuous learning.
* Collaborate with cross-functional teams (Data Science, Product, Analytics, Infrastructure, and Engineering) to deliver end-to-end data solutions.
* Drive technical roadmap and architectural decisions, ensuring scalability, performance, and long-term sustainability of data systems.
* Identify and implement improvements to ETL/ELT processes, focusing on automation, efficiency, and operational excellence.
* Evaluate and integrate emerging technologies to enhance data engineering capabilities and support evolving business needs.
* Oversee complex data projects, ensuring timely, high-quality delivery while balancing multiple priorities.
* Act as a subject matter expert on data modeling, pipeline optimization, large-scale data processing, and best practices.
* Ensure compliance with internal policies and external data regulations, promoting secure and responsible data usage across the team.
All About You:
* Extensive experience as a Data Engineer or in a similar role, with deep expertise in data engineering principles, data modeling, and pipeline development.
* Strong hands-on skills in SQL and experience with major RDBMS or cloud data warehouses.
* Strong hands on skills with AWS Data Engineering tech stack (EMR, Glue, Kinesis, Lambda etc.)
* Good hands on skill with orchestration tools like Apache Airflow
* Hands on experience with AWS AI and ML services like Sage maker, Bedrock
* Experience working with Databricks, delta and ICEBERG open table formats
* Proficiency with at least one programming or scripting language such as Python, Scala, or PowerShell.
* Experience working with big data and distributed systems (e.g., Spark, Hadoop, cloud-native big data services).
* Strong understanding of data quality frameworks, validation methods, and monitoring tools.
* Familiarity with Agile methodologies and modern DevOps practices for data engineering.
* Proven ability to lead technical teams, manage multiple projects, and work effectively across geographies and functions.
* Ability to translate complex business problems into scalable technical solutions.
* Excellent communication skills, with the ability to articulate data concepts to both technical and non-technical audiences.
* Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent practical experience).
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, an ... (truncated, view full listing at source)
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