Analytics Engineer, Global Strategy and Planning

About the Team:

The Global Strategy & Planning team supports the Delivery Leadership to define our strategy globally as well as working collaboratively across all functions and markets to assist with identifying areas to improve alignment and execution.

This role is analytics-focused as part of the Data Infrastructure pod. In this team, you will play a key role in streamlining various use cases including reporting, Ops analytics and automations or future GenAI use cases, on a global level. By building scalable data workflows and complex dashboards, while collaborating closely with engineering, data science and cross-functional stakeholders, you will enable fast data-driven decisions at Uber.

What You'll Do

• Data Pipeline Engineering: Write and maintain scalable data pipelines (ETL/ELT) to ensure timely availability of high quality data across systems.
• Reporting Infrastructure & Dashboards: Build automated and appealing reporting products to deliver actionable insights and enable interaction with critical metrics.
• Collaboration: Partner with data science, engineering, and business stakeholders (Ops) to understand data requirements and deliver scalable data solutions.
• Data Modelling: Develop data models to support and speed up Ops analytics.
• Tooling Creation: Design and implement automations to speed up Ops use cases.
• GenAI: Experiment the usage of GenAI to automate insight generation for a senior audience.

• MSc or 2+ years of experience in data analytics, analytics engineering, or similar roles.
• Strong in SQL for data manipulation, query optimization and metrics creation (e.g. Cube, Grouping Set, Window Functions).
• Proficiency in Python, particularly for scripting and automation.
• Problem-Solving: Ability to investigate data issues and implement solutions.
• Attention to Detail: A meticulous approach to ensuring data accuracy and quality.
• Communication: Sharing insights and findings to technical and non-technical senior stakeholders.

• Experience with orchestration tools (e.g., Apache Airflow, Prefect) and data processing frameworks (e.g. PySpark).
• Big plus for experience with Python-based dashboarding solutions (Streamlit, Flask).
• Experience with data visualization tools (e.g., Looker, Tableau).
• Familiarity with Data Modelling concepts (e.g. Star schema, entity centric modelling).
• Familiarity with AI/LLM tools and an interest in building AI-first solutions.
• Git/version control for managing codebases and deployment processes.
• Experience with cloud data warehousing (e.g. BigQuery, Snowflake, Redshift).