Job Title:

Lead Data Engineer

Overview:

Overview:
Mastercard’s Data Platforms and Engineering Services is seeking a Lead Data Engineer to join our team. As Lead Data Engineer you will have the opportunity to build high performance data pipelines to load into Mastercard Data Warehouse. Our Data Warehouse provides analytical capabilities to number of business users who help different customers provide answer to their business problems through data. You will play a vital role within a rapidly growing organization, while working closely with experienced and driven engineers to solve challenging problems.

Mastercard is the global technology company behind the world’s fastest payments processing network. We are a vehicle for commerce, a connection to financial systems for the previously excluded, a technology innovation lab, and the home of Priceless®. We ensure every employee can be a part of something bigger and to change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.
The Data Platforms and Engineering Services is focused on enabling insights into Mastercard network and help build data-driven products by curating and preparing data in a secure and reliable manner. Moving to a “Unified and Fault-Tolerant Architecture for Data Ingestion and Processing” is critical to achieving this mission.

Role:
• Partner on the design the next implementation of Mastercard secure, global data and insight architecture, building the data integration and processing capabilities and operationalizing “Unified Data Acquisition and Processing (UDAP) platform”
• Identify and proactively resolve performance bottlenecks in data systems.
• Collaborate with the customer support group to address performance issues in the field.
• Explore automation opportunities and develop tools to automate some of the day-to-day operations tasks.
• Maintain dashboards and provide performance metrics to monitor system health.
• Conceptualize and implement proactive monitoring where possible to catch issues early
• Experiment with tools to streamline development, testing, deployment, and data pipeline management.
• Work with cross functional agile teams to drive projects through full development cycle.
• Promote best practices in data engineering across teams.
• Collaborate with other data engineering teams to improve the data engineering ecosystem and talent within Mastercard.
• Creatively solve problems when facing constraints, whether it is the number of developers, quality or quantity of data, compute power, storage capacity or just time.
• Maintain awareness of relevant technical and product trends through self-learning/study, training classes and job shadowing.

All About You:
• Extensive background in data engineering, with a strong background in data pipeline architecture, ETL, and data modeling.
• Expertise in data warehousing solutions, database management, and optimization techniques, particularly product or service-based organization.
• Previous experience in Site Reliability Engineering or DevOps, along with software engineering or software architecture best practices.
• Strong skills in scalability, performance, and stability with expert knowledge in Linux, scripting (Shell and Python preferred) and troubleshooting complex systems.
• Operational experience with Big Data stacks Hadoop ecosystem, Spark is a plus), real-time data streaming frameworks (Kafka, NiFi), and network/server communication troubleshooting.
• Proficiency in performance tuning across databases, ETL jobs, and SQL scripts.
• Skilled in enterprise metrics/monitoring using tools like Splunk, Druid, and Grafana, with cloud computing experience, especially with Azure or AWS.
• Data-driven analytical mindset with a proven problem-solving track record.
• Agile methodologies experience.
• Proven track record in collaborating with cross-functional teams.
• Excellent written and verbal communication skills with the ability to convey complex technical concepts to a diverse audience.
• Ability to deal with multiple and competing priorities, structure and manage work streams, and set clear expectations and deadlines.
• Bachelor’s degree in Computer Science, Software Engineering, or a related field. Equivalent practical experience will be considered.


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