Engineering Manager II, Data & ML Systems

About the Role

As an Engineering Manager on the FinTech Data & ML Systems team, you will:

• Lead a high-performing team of data engineers and platform specialists in designing, implementing, and scaling data and ML solutions that power analytics, decision-making, and automation across FinTech.
• Drive the architecture and delivery of robust data pipelines, feature stores, and data platforms that enable machine learning and advanced analytics use cases.
• Collaborate closely with product managers, data scientists, and ML engineers to define and deliver reliable data and model workflows that support critical FinTech applications.
• Provide technical leadership in data architecture, ETL design, model training pipelines, and productionization of ML workflows.
• Identify opportunities to use data and ML to solve key business challenges, improve efficiency, and unlock new capabilities across payments, compliance, and financial systems.
• Promote a culture of technical excellence, encouraging best practices in system design, testing, observability, and maintainability across both data and ML domains.
• Mentor and develop engineers, fostering a collaborative, inclusive, and high-performance culture where teams can experiment, learn, and grow.
• Ensure reliability and scalability of FinTech data and ML systems through strong engineering discipline and well-defined operational practices.

• Proven experience as a Software or Data Engineering Manager, leading teams that deliver large-scale data infrastructure or platform solutions.
• Deep technical expertise in distributed data systems, including data ingestion, transformation, storage, and streaming.
• Working knowledge of machine learning workflows and supporting infrastructure (e.g., feature engineering, model training, deployment, and monitoring).
• Strong leadership, communication, and cross-functional collaboration skills - especially when partnering with analytics, data science, and product teams.
• Demonstrated ability to set vision, define roadmaps, and deliver data-driven solutions that support analytics and ML applications.
• Passion for mentoring engineers and fostering an environment of learning, innovation, and accountability.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• Experience designing or supporting data and ML infrastructure, such as feature stores, model registries, or experimentation platforms.
• Hands-on familiarity with big data and orchestration technologies (e.g., Spark, Airflow, Flink, Kafka, or equivalent).
• Understanding of ML operations (MLOps) and best practices for operationalizing models at scale.
• Experience in FinTech or Payments, especially in domains involving risk, fraud, compliance, or automation.
• Knowledge of data privacy, regulatory, and compliance requirements in financial systems.
• Advanced degree (Master's or PhD) in Computer Science, Engineering, or a related field.