### **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.


### **Basic Qualifications**

- 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 9+ 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**.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let’s move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

\*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to [accommodations@uber.com](mailto:accommodations@uber.com).