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.
Basic Qualifications
10+ years of experience and 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 with 10+ years of experience
Preferred Qualifications
9+ years of 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 .
