Senior Data Engineer

Enterprise Engineering Inc. (EEI); Missouri; On-site; USD 120,000 - 170,000; Full time

Job summary

Mastercard seeks an experienced Data Engineer to design, develop, test, and deploy scalable data pipelines using Spark and Java/Scala across Hadoop and cloud storage platforms.

You will build batch and near real-time processing frameworks for petabyte-scale workloads and develop reusable components that accelerate data product delivery while meeting enterprise standards.

Qualifications

• 7+ years of enterprise-scale data warehousing/lakehouse experience required.

Responsibilities

• Lead end-to-end development of data pipelines from design to production support.

Skills

Data Engineering

Spark

Java/Scala

Hadoop

Airflow

NiFi

SQL/NoSQL

AWS

Data Quality

Security

Tools

Apache Spark

Apache NiFi

Airflow

S3/EMR

Cloud GLUE

Oracle

SQL Server

Job description

Design, develop, test, and deploy high-quality, secure, scalable, and resilient data pipelines using Apache Spark, Java/Scala across Hadoop and cloud-native object storage platforms.

Build and maintain batch and near real-time data processing frameworks capable of supporting petabyte-scale workloads.

Develop reusable engineering components and frameworks that accelerate data product delivery while maintaining enterprise standards.

Architecture & Platform Engineering

• Design and implement a \"build once, run anywhere\" architecture supporting seamless deployment across on-premises and public cloud environments without code changes.
• Implement data lineage, metadata management, data cataloguing, data quality controls, and observability capabilities across the data ecosystem.
• Collaborate with architects and platform teams to establish scalable design patterns and engineering best practices.
• Contribute to migration initiatives moving legacy ETL and data warehouse workloads from on-premises environments to cloud-native architectures.
• Leverage cloud services such as Amazon S3, EMR, Glue, and related data services to improve scalability, reliability, and operational efficiency.
• Drive adoption of modern lakehouse and distributed compute architectures.

Delivery & Technical Leadership

• Lead end-to-end development activities including requirement analysis, solution design, coding, testing, deployment, and production support.
• Mentor and guide junior engineers through code reviews, technical coaching, and engineering best practices.
• Partner with product owners, analysts, architects, and business stakeholders to deliver high-quality solutions within committed timelines.

Operational Excellence

• Troubleshoot complex production incidents and perform root cause analysis to identify and implement long-term remediation strategies.
• Ensure compliance with Mastercard's engineering, security, quality assurance, and operational governance standards.
• Continuously identify opportunities to improve performance, automation, monitoring, and process efficiency.
• Evaluate emerging data technologies and conduct proof-of-concept (POC) initiatives to determine their applicability within Mastercard's data ecosystem.
• Contribute to engineering innovation and continuous improvement initiatives across the organisation.

Qualifications

Required Experience

• 7+ years of experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.
• Proven experience implementing multiple end-to-end data engineering projects within large-scale distributed computing environments.
• Hands-on experience migrating ETL and analytics workloads from on-premises platforms to cloud-native environments.

Technical Expertise

• Strong development experience using:Apache Spark, Scala or Java, Hadoop ecosystem technologies, Object Storage platforms
• Experience building orchestration and workflow solutions using: Apache Airflow/ Apache NiFi and Similar enterprise scheduling frameworks
• Strong SQL expertise and experience with Relational and NoSQL database technologies: Oracle/ SQL Server, Cassandra, Dynamo DB etc.
• Working knowledge of cloud platforms, preferably AWS: Amazon S3, EMR, AWS Glue, Cloud-native data services

Professional Skills

• Strong analytical and problem-solving capabilities.
• Experience operating within Agile delivery environments.
• Excellent written and verbal communication skills.
• Proven ability to collaborate within geographically distributed and matrix-based teams.
• Self-starter with strong ownership, accountability, and execution focus.
• Ability to learn emerging technologies quickly and apply them effectively to business challenges.