Lead Data Platform Engineer – Credit Risk & Analytics

Mastercard; Dublin; On-site; EUR 110,000 - 160,000; Full time

Job summary

Mastercard is seeking a Lead Data Engineer for the Enterprise Credit Risk (ECR) team to build and scale data platforms powering credit decisioning and analytics. You will lead design and implementation of data pipelines across cloud environments, ensuring data quality, governance, and operational excellence.

The role collaborates with Product, Risk Analytics, Data Science, Architecture, and Business stakeholders to deliver trusted data for AI-driven insights and risk management across

Qualifications

• Designing and implementing large-scale data engineering solutions.
• Experience with cloud-based data platforms on Azure, AWS, or GCP.
• Experience with ETL/ELT pipelines, streaming architectures, and data integration frameworks.
• Strong data modeling knowledge for analytical and operational workloads.
• Experience with data quality, lineage, metadata management and governance.

Responsibilities

• Lead design and evolution of enterprise-grade data platforms and pipelines.
• Architect and implement scalable ETL/ELT frameworks using Databricks, Spark, Delta Lake, and cloud-native tech.
• Establish data quality, governance, observability, and monitoring for trusted data products.
• Drive migration of legacy assets to cloud-based Data Lakehouse architectures.
• Mentor engineers and promote engineering standards and best practices.
• Lead technical design reviews and influence data architecture direction.

Skills

Databricks

Apache Spark

Delta Lake

SQL

Python

Airflow

Hadoop

Cloud platforms

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, or related STEM

Tools

Databricks

Apache Spark

Delta Lake

SQL

Python

Airflow

Hadoop

Job description

Mastercard is seeking a Lead Data Engineer for the Enterprise Credit Risk (ECR) team to build and scale data platforms powering credit decisioning and analytics. You will lead design and implementation of data pipelines across cloud environments, ensuring data quality, governance, and operational excellence.

The role collaborates with Product, Risk Analytics, Data Science, Architecture, and Business stakeholders to deliver trusted data for AI-driven insights and risk management across