Data Analyst I, Tech

About the role and team

Working at Uber means solving hard problems in a high-stakes, fast-moving environment. As a Data Analyst, you will help drive the analysis behind critical decisions related to operations, process, and optimizations. You will turn unstructured problems with lots of ambiguity into focused plans of execution, creating analytical frameworks to support new initiatives and products.

This role requires you to be a big-picture thinker and strategic partner who can move with urgency and care. You’ll need to take ownership of your work, stay adaptable through constant change, and demonstrate resilience when navigating the messy reality of doing big things at scale. If you are energized by challenge and motivated by real-world impact, this is where you will grow.

Rate of Pay: $113,000 to $138,000 per Year

You will be eligible to participate in Uber's bonus program, and may be offered other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.

What you’ll do

• Partner with Customer Support, Engineering, and Product teams to gather and clarify business needs for analytics and reporting related to autonomous vehicle (AV) customer support.
• Lead discovery and gap analysis to assess existing analytics coverage, data availability, and reporting workflows, and identify opportunities for improvement.
• Document detailed business requirements, data requirements, metric definitions, and reporting specifications for new dashboards, reports, and analytics tools.
• Translate business and analytics requirements into data solution requirements, defining data structures, context, and usage to support customer support quality measurement.
• Perform source-to-target data mapping to support the design and implementation of new data models and data pipelines.
• Support the design and development of scalable analytics solutions by ensuring alignment between business intent and technical implementation.
• Participate in user acceptance testing (UAT) and data validation activities to ensure new data products meet functional requirements and established data quality standards.
• Maintain and update documentation for data sources, metric definitions, reporting logic, and data lineage to support long-term scalability and maintainability.
• Serve as a liaison between technical and non-technical stakeholders to align on goals, priorities, dependencies, and delivery timelines.
• Contribute to the continuous improvement of data analytics infrastructure, data processes, and reporting workflows supporting autonomous vehicle customer support.
• May telecommute.

Basic Qualifications

• Employer will accept a Bachelor's degree in Data Science, Computer Science, Information Science, Information Systems, Statistics, Engineering, Analytics, or related field, and two years of experience in the job offered or in a related occupation.
• Position requires two years in:
  • Translating complex customer support and product workflows in autonomous vehicle, ride-sharing, and delivery ecosystems into precise data, analytics, and reporting requirements to support operational and customer experience decisions;
  • Conducting end-to-end discovery, process mapping, and source-to-target data mapping across multiple systems to design scalable, auditable data models and analytics pipelines;
  • Using SQL to query, reconcile, and validate large-scale structured and semi-structured datasets, including identifying and resolving data quality issues in production analytics environments;
  • Performing scalable, repeatable data analysis using Python or R, to build reusable analytical frameworks;
  • Designing, executing, and interpreting A/B tests, simulations, or causal analyses to evaluate the impact of operational, tooling, or process changes in customer support environments;
  • Knowledge of autonomous vehicle technology and real-world customer support operations in transportation, ride-sharing, or food and grocery delivery environments, including service quality and operational efficiency;
  • Building and maintaining self-service dashboards and analytics using tools such as Tableau or Looker;
  • Validating, monitoring, and debugging analytics outputs in production by tracing issues across upstream data sources, transformation logic, and reporting layers.