Manager Data Engineering

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.

As a Manager Data Engineering in the Data and Services Product Foundations team, you will manage develop data & analytics solutions that sit atop vast datasets gathered by retail stores, restaurants, banks, and other consumer-focused companies. The challenge will be to create high-performance algorithms, cutting-edge analytical techniques and intuitive workflows that allow our users to derive insights from big data that in turn drive their businesses. You will have the opportunity to create high-performance analytic solutions based on data sets measured in the billions of transactions to unleash the value of big data.

You will have the opportunity to manage and develop data-driven innovative analytical solutions and identify opportunities to support business and client needs in a quantitative manner and facilitate informed recommendations/decisions through activities like building automated data pipelines, designing data architecture/schema, performing jobs in big data cluster by using different execution engines and program languages such as Hive/Impala, Python, Spark, R, etc

Your Role:

Manage mid-size team of Data Engineers, overall coordination, status reporting, schedules and stability of project-oriented work efforts, ensuring that project goals and objectives are met within agreed-upon time, scope and resource requirements.
Develop detailed project plans and manage all implementation processes.
Regularly communicate to senior management regarding the status of specific project.
Lead and manage the team including performance review and technical guidance.

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.

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 on Data engineering best practices.

Design, develop, implement, and debug large and complex data platforms.

Implement new technologies, policies and practices that can help increase resiliency, performance, 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 scieng6t6ce and engineering.

Drives 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

Maintain awareness of relevant technical and product trends through self-learning/study, training classes, and job shadowing.

Ideal Candidate Qualifications:

Strong People management, leadership, organizational skills.
Outstanding communication (written and verbal) skills.

Experience leveraging open-source tools, predictive analytics, 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.

Expert proficiency in using Python/Scala, Spark (tuning jobs), SQL, Hadoop platforms to build Big Data products & platforms.

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 years leading collaborative work in complex engineering projects in an Agile setting eg Scrum.

Extensive data warehousing/data lake development experience with strong data modelling 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, modelling, tuning, evaluating, monitoring, and presenting.

At least 10 years of relevant hands-on experience as a Data Engineer in an individual contributor capacity.

At least a bachelor s degree in computer architecture, Computer Science, Electrical Engineering or equivalent experience. Postgraduate degree is an advantage.

Corporate Security Responsibility

• 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.