Senior Data Engineer

Mastercard

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Quick summary

Work type: On-site
Location: Atlanta, GA
Salary: $115,000–$184,000 / yr
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $169k

This role $150k

$104k most similar roles pay here $221k

This role pays less than 69% of similar roles. Most pay $135,675–$202,561 — the shaded band above. At the midpoint, this role pays about $150k versus about $169k 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 132 open roles on FindRole.

Listed pay typically runs $121,000–$205,500 across 118 roles with salary data.

Most-posted roles

View all roles at Mastercard

At a glance

TL;DR · Senior Data Engineer

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The Senior Data Engineer joins the Marketing Services Technology team to build scalable data platforms, pipelines, and services that power marketing measurement, customer insights, campaign optimization, and AI/ML capabilities. This role involves designing and maintaining batch, streaming, and real-time data pipelines while developing reusable frameworks for ingestion, transformation, and orchestration. The engineer will manage enterprise data lake, warehouse, and lakehouse solutions while migrating legacy platforms to cloud-native architectures. Key responsibilities include implementing data quality, governance, and security measures while establishing standards for CI/CD and infrastructure as code. Required technical skills include proficiency in SQL and programming languages like Python, Java, or Scala, alongside experience with Spark, Kafka, Hadoop, and cloud platforms such as AWS, Azure, or GCP. The role solves complex problems regarding data lifecycle management and reporting within the marketing services domain.

What does a Data Engineer earn?

Median $170950 from 215 postings across 56 companies.

See salary data

Skills

SQL Python Java Scala Spark Kafka Hadoop AWS Azure GCP CI/CD Infrastructure as Code ETL ELT Data Governance Git DevOps Machine Learning Data Lake Data Warehouse

What you'll do

Design, build, and maintain scalable batch, streaming, and real-time data pipelines.
Develop reusable frameworks for data ingestion, transformation, orchestration, and delivery.
Build and support enterprise data lake, warehouse, and lakehouse solutions.
Implement data quality, observability, governance, security, and monitoring capabilities.
Migrate legacy platforms to modern cloud-native architectures.
Establish engineering standards for CI/CD, infrastructure as code, testing, and automation.
Troubleshoot complex production issues and lead root-cause analysis to improve service reliability.
Mentor engineers and provide technical leadership across data initiatives.

What we're looking for

Bachelor's degree or equivalent experience in computer science, engineering, information systems, or a related technical field.
Strong experience designing scalable data platforms and distributed data-processing solutions.
Proficiency in SQL and at least one programming language such as Python, Java, or Scala.
Experience with large-scale data-processing frameworks like Spark, Kafka, or Hadoop.
Experience building data pipelines for analytics, reporting, machine learning, and operational products.
Strong understanding of data modeling, ETL/ELT patterns, architecture, and lifecycle management.
Experience with cloud platforms such as AWS, Azure, or GCP.
Knowledge of data governance, security, privacy, lineage, metadata management, and regulatory requirements.
Experience with Git, CI/CD, DevOps automation, infrastructure as code, testing, and observability.
Experience leading technical initiatives, influencing architectural decisions, or mentoring engineers (preferred).
Experience modernizing legacy data platforms or supporting cloud migration programs (preferred).
Exposure to marketing technology, campaign measurement, customer insights, or marketing analytics (preferred).
Advanced degree in a related technical discipline (preferred).

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