Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Senior Data Engineering
Overview
Ethoca, a Mastercard company, is seeking a Senior Data Engineer to join the First Party Trust (FPT) team. First Party Trust serves Merchants, Financial Institutions (Issuers and Acquirers), and Digital Partners by enabling the identification and mitigation of First Party Fraud across the payment’s ecosystem.
The platform leverages modern cloud-based data technologies to ingest, process, secure, enrich, and analyze large-scale transactional and behavioral data. The team develops scalable data pipelines, data products, analytics capabilities, and AI-ready data platforms while ensuring compliance with Mastercard security, privacy, and data governance standards.
Our technology stack includes Snowflake, Databricks, Apache Airflow, Apache NiFi, Azure Data Factory (ADF), UDAP, SQL, Python, Spark, Azure Cloud Services, File Transfer Technologies, Splunk, Dynatrace, Terraform, and Data Security Platforms.
Are you motivated by solving complex data challenges at enterprise scale?
• Do you enjoy building highly scalable and resilient data platforms?
• Do you want to leverage cloud-native data technologies to enable analytics, AI, and machine learning solutions?
Do you want to be part of a team transforming how global merchants and financial institutions combat fraud through data-driven insights?
We are looking for innovative problem-solvers who thrive in a collaborative environment and are passionate about building secure, scalable, and reliable data solutions.
Role
Design, develop, implement, and maintain enterprise-scale data pipelines, data platforms, and analytical data products that support business intelligence, fraud analytics, machine learning initiatives, and operational reporting. Partner with business, product, architecture, and engineering teams to ensure secure, scalable, and compliant data solutions across the organization.
Major Accountabilities
• Design and develop scalable batch, streaming, and near-real-time data pipelines using modern cloud data engineering technologies.
• Build, maintain, and optimize enterprise data platforms leveraging Snowflake, Databricks, Spark, Airflow, NiFi, ADF, and related technologies.
• Design and implement robust ETL/ELT frameworks for ingesting, transforming, validating, and delivering data across multiple business domains.
• Collaborate with business stakeholders, data consumers, product owners, and architects to understand data requirements and deliver reliable data solutions.
• Develop and maintain data models, data marts, and curated datasets that support analytics, reporting, AI, and machine learning use cases.
• Integrate and manage data from multiple internal and external sources while ensuring data quality, consistency, lineage, and governance.
• Implement data orchestration and workflow automation using Apache Airflow, Azure Data Factory, NiFi, and related technologies.
• Develop and optimize Spark and Databricks workloads to process large-scale datasets efficiently.
• Monitor and tune data pipelines, database workloads, and platform performance to maximize scalability, reliability, and cost efficiency.
• Implement enterprise data governance standards, including data quality controls, metadata management, and lineage tracking.
• Ensure compliance with PCI-DSS, privacy regulations, Mastercard security standards, and enterprise data classification requirements.
• Implement and maintain encryption, decryption, tokenization, masking, and secure data handling processes for sensitive and regulated data.
• Support secure file transfer mechanisms and integrations using SFTP, managed file transfer solutions, APIs, and cloud-native services.
• Develop resilient data recovery, disaster recovery, and operational support processes across critical data platforms.
• Support production environments through incident management, root-cause analysis, troubleshooting, and continuous service improvement activities.
• Analyze platform operational metrics using Splunk, Dynatrace, and monitoring tools to identify reliability and performance improvement opportunities.
• Participate in architecture reviews, code reviews, design reviews, and technology evaluations.
• Create and maintain technical documentation, data flow diagrams, architecture artifacts, design specifications, and operational runbooks.
• Support AI and machine learning initiatives by building trusted, high-quality, feature-rich datasets and data products.
• Evaluate emerging technologies and contribute to continuous improvement of the organization's data engineering strategy and roadmap.
• Mentor junior data engineers and contribute to engineering best practices, coding standards, and technical excellence.
• Partner with global teams across the United States, Canada, Dublin, and India to deliver high-quality data solutions.
All About You
Required Qualifications
Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field.
6+ years of experience in Data Engineering, Big Data, Data Warehousing, or Analytics Engineering.
Strong experience designing and implementing large-scale ETL/ELT data pipelines.
Hands-on expertise with Snowflake Data Cloud.
Strong experience with Databricks and Apache Spark.
Advanced SQL development and performance tuning skills.
Strong Python programming experience for data engineering and automation.
Experience with Apache Airflow workflow orchestration.
Experience with Apache NiFi and/or Azure Data Factory (ADF).
Experience with enterprise data warehousing concepts, dimensional modeling, and data architecture.
Experience handling large-scale structured and semi-structured datasets.
Strong understanding of data governance, data quality, metadata management, and lineage concepts.
Experience working with PCI-regulated environments and sensitive customer data.
Knowledge of PII data classification, data privacy controls, and data protection requirements.
Experience implementing encryption, decryption, key management, data masking, and secure data processing solutions.
Experience with secure file transfer technologies including SFTP and managed file transfer solutions.
Familiarity with CI/CD, DevOps, Infrastructure as Code, and Terraform.
Experience using monitoring platforms such as Splunk and Dynatrace.
Excellent communication, documentation, analytical, and problem-solving skills.
Ability to work effectively across global teams and time zones.
Preferred Qualifications
Experience with Azure Cloud Services and cloud-native data platforms.
Experience supporting AI, machine learning, and advanced analytics initiatives.
Experience with Java or Spring Boot development.
Experience with streaming technologies and event-driven architectures.
Experience with fraud, payments, fintech, or financial services platforms.
Snowflake, Databricks, Azure, or related industry certifications.
Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
Abide by Mastercard’s 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 Mastercard’s guidelines.