Job
Description
Dynamic Yield, a Mastercard company, is a global technology company that powers the world's fastest payments processing network. Our Mastercard Services organization is a key differentiator, providing cutting-edge solutions used by some of the world's largest organizations to make multi-million-dollar decisions and grow their businesses. Our Advanced Analytics program, a new and rapidly growing initiative, offers products that provide accurate perspectives on Credit Risk, Portfolio Optimization, and Ad Insights. We are looking for an innovative Lead Data Engineer to drive the technical design and development of an Analytic Foundation . This foundation will be a suite of individually commercialized analytical capabilities, including a comprehensive data platform delivered via APIs. You will partner closely with other business areas to build and enhance solutions that deliver significant value to our customers, working in small, flexible teams with a focus on end-to-end contribution from UI to data architecture. Position Responsibilities As a Lead Data Engineer within the Advanced Analytics team, you will: Data Science Collaboration: Lead collaboration with data scientists to understand existing modeling pipelines and identify optimization opportunities. Data Integration & Management: Oversee the integration and management of data from various sources and storage systems, establishing robust processes and pipelines to produce cohesive datasets for analysis and modeling. User-Centric Development: Partner with Product Managers and Customer Experience Designers to develop a deep understanding of users and use cases, applying that knowledge to scope and build new modules and features. Automation & Optimization: Design and develop data pipelines to automate repetitive tasks within data science and data engineering. Identify patterns and innovative solutions in existing spaces, consistently seeking opportunities to simplify, automate tasks, and build reusable components for multiple use cases and teams. Cross-Functional Leadership: Demonstrate experience leading cross-functional teams or working across different teams to solve complex problems. Data Product Creation: Create data products that are well-modeled, thoroughly documented, and easy to understand and maintain. Project Leadership: Be comfortable leading projects in environments with undefined or ambiguous requirements. Mentorship: Mentor junior data engineers, fostering their growth and skill development. Ideal Candidate Qualifications Education: Bachelor's degree in Computer Science, Software Engineering, or a related field. Big Data Expertise: High proficiency in using Python or Scala, Spark, Hadoop platforms & tools (Hive, Impala, Oozie, Airflow, NiFi, Sqoop), and SQL to build Big Data products & platforms. Spark Proficiency: Extensive experience with the Spark Processing engine . Programming Languages: Proficiency in at least one modern programming language such as Python, Java, or Scala . Computer Science Fundamentals: Strong Computer Science fundamentals in object-oriented design, data structures, algorithm design, problem-solving, and complexity analysis. Business Acumen: Ability to easily move between business, data management, and technical teams; quickly intuiting the business use case and identifying technical solutions to enable it. Software Development Knowledge: Working Knowledge in Software Development engineering Paradigms along with Data Engineering. Database Experience: Experience with both Relational Databases and NoSQL databases. Cloud Technologies: Experience in cloud technologies like Databricks/AWS/Azure . Scripting & Systems: Basic Shell scripting and knowledge of Linux/Unix systems. Scalable Software Design: Experience in designing & developing software at scale. Communication: Strong written and verbal English communication skills.
