Lead Data Engineer

Lead Data Engineer Job Title
Lead Data Engineer
Overview
Who is Mastercard
Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships, and passion, our innovations help individuals, financial institutions, governments, and businesses realize their greatest potential.
The Mastercard Services organization is a key differentiator, delivering cutting-edge solutions used by some of the worlds largest organizations to make critical business decisions. Focused on innovation and scale, Services provides data-driven capabilities across consulting, analytics, experimentation, and risk management.
Role Overview
Data Platform Orchestration is seeking a Lead Data Engineer to design and build next-generation, cloud-native data platforms supporting Mastercards global data ecosystem.
In this role, you will lead the development of scalable batch and real-time data pipelines, enabling efficient data processing across Data Lakes and Data Warehouses. You will work at the intersection of data engineering, cloud platforms, and distributed systems, contributing to high-impact initiatives and driving engineering excellence.
This role is ideal for someone who thrives in a fast-paced, collaborative environment, enjoys solving complex data challenges, and is passionate about building resilient, high-performance systems at scale.
Key Responsibilities
Design and build scalable batch and real-time data pipelines using Spark, Kafka, and (preferred) Apache Flink
Develop robust ETL/ELT frameworks for structured and unstructured data
Build and optimize data ingestion and transformation pipelines for Data Lakes and Data Warehouses
Implement stream processing solutions for near real-time use cases
Ensure data quality, lineage, observability, and governance across pipelines
Optimize data jobs for performance, scalability, and cost efficiency
Design and operate cloud-native data platforms on AWS, Azure, or GCP
Leverage managed services such as S3/ADLS/GCS, EMR/Databricks, BigQuery/Redshift/Snowflake
Implement Infrastructure as Code (Terraform, CloudFormation, or equivalent)
Ensure high availability, fault tolerance, and disaster recovery
Drive cost optimization strategies for large-scale data workloads
Implement secure data access controls aligned with enterprise standards
Platform Engineering Excellence
Build reusable data frameworks, libraries, and pipeline templates
Drive adoption of CI/CD, automated testing, and observability
Develop and enhance developer tooling and platform capabilities
Contribute to cloud-agnostic platform architecture and automation
Technical Leadership Collaboration
Provide technical leadership, mentorship, and design guidance
Conduct code reviews, architecture reviews, and best practice enforcement
Collaborate with architects, product owners, and cross-functional teams
Act as a Subject Matter Expert (SME) for data platform initiatives
Promote engineering excellence through documentation, design standards, and innovation
Work effectively across globally distributed teams
Required Skills Qualifications
Strong proficiency in Object-Oriented Programming and Design (OOP/OOAD) Java (JDK 8+); Python and/or Go is a plus
Experience building data services and distributed systems
Strong understanding of multithreading, scalability, and performance tuning
Strong hands-on experience with AWS, Azure, or GCP
Experience with cloud-native data services (S3, ADLS, GCS, Databricks, EMR, BigQuery, Redshift)
Strong experience with Apache Spark (Core, SQL, Structured Streaming)
Hands-on experience with Kafka or equivalent messaging platforms
Experience with real-time processing frameworks (Apache Flink preferred or Spark Streaming)
Strong understanding of ETL/ELT design patterns and pipeline architectures
Experience with data formats (Parquet, Avro, ORC)
Knowledge of data modeling (dimensional modeling, star/snowflake schemas)
Proficiency in Infrastructure as Code (Terraform, CloudFormation, ARM templates)
Experience with Docker and Kubernetes
Solid understanding of cloud networking, IAM, and security best practices
Experience with workflow orchestration tools (Airflow or equivalent)
Strong SQL skills and experience with Data Warehouse platforms
Understanding of data governance, lineage, and observability frameworks
Experience with CI/CD tools (Jenkins, GitHub Actions, etc.)
Strong testing practices (JUnit or equivalent frameworks)
Experience with monitoring observability (Splunk, Dynatrace, Prometheus, etc.)
Familiarity with performance testing tools (JMeter, Gatling)
Understanding of secure development practices (PCI DSS, GDPR, etc.)
Proven ability to lead and mentor engineering teams
Strong problem-solving and system design skills
Passion for innovation, automation, and continuous improvement
Ability to operate effectively in a fast-paced, global environment

Education
Bachelors degree in Computer Science, Information Technology, Engineering, or a related field

• Abide by Mastercards security policies and practices;
• Ensure the confidentiality and integrity of the information being accessed;
• Report any suspected information security violation or breach, and
• Complete all periodic mandatory security trainings in accordance with Mastercards guidelines.