Hybrid Lead Data Engineer: Scale Trusted Data Platforms

Master Card; Town of Montana (WI); Hybrid; USD 140,000 - 231,000; Full time

Job summary

Mastercard is seeking a Lead Data Engineer in O'Fallon, MO to design and scale data platforms for credit risk, analytics, and AI-driven insights. You will blend hands-on engineering with technical leadership to deliver scalable, governed data products across cloud platforms and large data processing.

The role requires partnering with product, risk analytics, data science, architecture, and engineering teams to translate complex needs into robust solutions.

Qualifications

• Deep experience designing large-scale data engineering solutions and distributed processing.
• Proficient with Databricks, Spark/PySpark, Delta Lake, SQL and Python.
• Experience building cloud-based data platforms using Azure, AWS, or GCP.
• Strong data modeling and data architecture for analytical and operational workloads.
• Experience implementing data quality, lineage, metadata, observability and governance.

Responsibilities

• Lead design and evolution of enterprise data platforms and pipelines for risk, analytics, and reporting.
• Architect and implement scalable ETL/ELT frameworks using Databricks and Spark.
• Drive cloud transformation and data lakehouse modernization.
• Establish data quality, lineage, governance, observability and monitoring.
• Mentor engineers and shape engineering standards across teams.

Skills

Databricks

Apache Spark/PySpark

Delta Lake

SQL

Python

Azure

AWS

GCP

Data governance

Data modeling

Education

Bachelor's degree in Computer Science or related STEM

Tools

Hadoop

GitHub / CI-CD tooling

Airflow

Job description

Mastercard is seeking a Lead Data Engineer in O'Fallon, MO to design and scale data platforms for credit risk, analytics, and AI-driven insights. You will blend hands-on engineering with technical leadership to deliver scalable, governed data products across cloud platforms and large data processing.

The role requires partnering with product, risk analytics, data science, architecture, and engineering teams to translate complex needs into robust solutions.