Lead Data Engineer

Mastercard

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
O'Fallon, MO
Salary
$140,000–$231,000 / yr
Posted
41 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $164k
This role $186k
$114k $244k
below market most similar roles pay here above market

This role pays more than 63% of similar roles. Most pay $126,800–$202,200 — the blue band above. At the midpoint, this role pays about $186k versus about $164k for comparable roles.

Based on 240 similar postings.

Employer

About Mastercard

Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services

Mastercard currently has 150 open roles on FindRole.

Listed pay typically runs $122,000–$207,000 across 142 roles with salary data.

Most-posted roles

View all roles at Mastercard

At a glance

TL;DR · Lead Data Engineer

The Lead Data Engineer joins the Enterprise Credit Risk team to design and scale next-generation data platforms powering credit decisioning, portfolio risk management, regulatory reporting, and AI-driven insights. You will lead the architecture and implementation of scalable ETL/ELT frameworks, drive cloud transformation by migrating legacy assets to Data Lakehouse architectures, and establish data quality, lineage, and governance capabilities. The role requires building reliable data products for lending and risk ecosystems while mentoring engineers and defining technical roadmaps. Key technologies include Databricks, Apache Spark, PySpark, Delta Lake, SQL, Python, and Apache Airflow. You will also utilize Azure, AWS, or GCP, along with Kafka, Parquet, Avro, and ORC. The position involves solving complex business needs by transforming requirements into sustainable technical solutions for sensitive financial data.

What you'll do

  • Design and develop enterprise-grade data platforms and pipelines for credit risk products and regulatory reporting.
  • Architect scalable ETL/ELT frameworks using Databricks, Apache Spark, Delta Lake, and cloud-native technologies.
  • Lead the migration of legacy data assets to modern cloud-based Data Lakehouse architectures.
  • Establish data quality, lineage, metadata, governance, and observability capabilities for reliable data products.
  • Define engineering standards for coding, testing, deployment automation, and operational excellence.
  • Optimize large-scale data workloads for performance, reliability, scalability, and cost efficiency.
  • Build reusable data products that enable AI, machine learning, and advanced analytics.
  • Mentor and coach engineers by providing technical guidance, design expertise, and knowledge sharing.

What we're looking for

  • Deep expertise in designing and implementing large-scale data engineering solutions and distributed data processing systems.
  • Advanced proficiency with Databricks, Apache Spark/PySpark, Delta Lake, SQL, and Python.
  • Experience building and operating cloud-based data platforms using Azure, AWS, or GCP.
  • Experience developing enterprise-grade ETL/ELT pipelines, data integration frameworks, and streaming architectures.
  • Experience implementing data quality, lineage, metadata management, observability, and data governance frameworks in enterprise environments.
  • Working knowledge of CI/CD, infrastructure-as-code, automated testing, Git/GitHub, and DevOps practices.
  • Experience with workflow orchestration tools such as Apache Airflow.
  • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related STEM discipline (preferred).

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