Sr Manager, Data Engineering
Uber
- Location
- Hyderabad, Telangana, India
- Job type
- Full-time
Required skills
- advanced analytics
- Airflow
- compliance
- cross-functional
- data architecture
- data ingestion
- data science
- ETL
- fintech
- Flink
- Kafka
- machine learning
- Spark
About the role
Uber
Website:
uber.com
Job details:
About The Role
As an
Engineering Manager on the FinTech Data & ML Systems team, you will:
- Lead a high-performing team of 25+ 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.
---- What the Candidate Will Do ----
- Lead a high-performing team of 25+ 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.
Basic Qualifications
- 15+ 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
- 15+ 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.
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