Lead Data Engineer

Our Purpose

We work 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 and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Title and Summary

Lead Data Engineer

Mastercard Overview
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 change lives. We believe as our company grows, so should you. We believe in connecting everyone to endless, priceless possibilities.


Your Role:

• Lead a mid-size team of Data Engineers.
• Create and maintain optimal data pipeline architecture.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using ETL processes and modern cloud technologies.
• Take ownership or clarification of requirements and solutions proposition before implementation.
• Lead the building of scaled machine learning production systems by designing pipelines and engineering infrastructure.
• Facilitate the development and deployment of offline ML models into production through the use of scalable tools and services to handle machine learning training and inference processes.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Keep up to date with latest open-source tools for data engineering.
• Mentor junior team members in ML Production best practices.
• Design, develop, implement, and debug large and complex data platforms.
• Analyze and improve performance of existing platforms.
• Implement new technologies, policies and practices that can help increase resiliency, automation and improve platform health.
• Identifying technology gaps and help business build and deliver viable solutions.
• Drive the evolution of Data & Services products/platforms with an impact-focused on data science and engineering.
• Participate in the development of data and analytic infrastructure for product development.
• Continuously innovate and determine new approaches, tools, techniques & technologies to solve business problems and generate business insights & recommendations.
• Partner with roles across the organization including consultants, engineering, and sales to determine the highest priority problems to solve.
• Evaluate trade-offs between many possible analytics solutions to a problem, taking into account usability, technical feasibility, timelines, and differing stakeholder opinions to make a decision.
• Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients, and other stakeholders.
• Ensure proper data governance policies are followed by implementing or validating Data Lineage, Quality checks, classification, etc.
• Work with small, cross-functional teams to define the vision, establish team culture and processes.
• Consistently focus on key drivers of organization value and prioritize operational activities accordingly.
• Maintain awareness of relevant technical and product trends through self-learning/study, training classes, and job shadowing.

Ideal Candidate Qualifications:

Experience leveraging open-source tools, predictive analytics, machine learning, Advanced Statistics, and other data techniques to perform basic analyses.
• Demonstrated basic knowledge of statistical analytical techniques, coding, and data engineering.
• Experience developing and configuring dashboards is a plus.
• Demonstrated judgement when escalating issues to the project team.
• High proficiency in Python/Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi), SQL.
• Curiosity, creativity, and excitement for technology and innovation.
• Demonstrated quantitative and problem-solving abilities.
• Ability to multi-task and strong attention to detail.
• Motivation, flexibility, self-direction, and desire to thrive on small project teams.
• Expert proficiency in using Python/Scala, Spark(tuning jobs), SQL, Hadoop platforms to build Big Data products & platforms.
• Experience with data pipeline and workflow management tools: NIFI, Airflow.
• Comfortable in developing shell scripts for automation.
• Proficient in standard software development, such as version control, testing, and deployment.
• Experience with visualization tools like tableau, looker.
• At least 5 year leading collaborative work in complex engineering projects in an Agile setting e.g. Scrum.
• Extensive data warehousing/data lake development experience with strong data modeling and data integration experience.
• Good SQL and higher-level programming languages with solid knowledge of data mining, machine learning algorithms and tools.
• Strong hands-on experience in Analytics & Computer Science.
• Demonstrated basic knowledge of statistical analytical techniques, coding, and data engineering.
• Experience in building and deploying production-level data-driven applications and data processing workflows/pipelines and/or implementing machine learning systems at scale in Java, Scala, or Python and deliver analytics involving all phases like data ingestion, feature engineering, modeling, tuning, evaluating, monitoring, and presenting.
• Outstanding communication and organizational skills.
• Strong English written and verbal communication skills.
• At least 10 years of relevant hands-on experience as a Data Engineer in an individual contributor capacity.
• Able to lead the implementation of machine learning production systems.
• Demonstrated ability, through hands-on experience, to develop production machine learning pipelines.
• At least a bachelor’s degree in computer architecture, Computer Science, Electrical Engineering or equivalent experience. Postgraduate degree is an advantage.

The following skills will be considered as a plus
• Hands-on experience with cloud computing and big data frameworks e.g. GCP, AWS, Azure, Flink, Elasticsearch, and Beam
• Knowledge in MLOps frameworks such as TensorFlow Extended, Kubeflow, or MLFlow
• Financial Institution or a Payments experience a plus
• Experience in managing/working in Agile teams
• Experience developing and configuring dashboards
#AI3

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.