Company Overview

DiDi Global Inc. is the world’s leading mobility technology platform. It offers a wide range of app-based services across markets including Asia-Pacific, Latin America and Africa, including ride hailing, taxi hailing, chauffeur, hitch and other forms of shared mobility as well as auto solutions, food delivery, intra-city freight, and financial services.

DiDi provides car owners, drivers, and delivery partners with versátil work and income opportunities. It is committed to collaborating with policymakers, the taxi industry, the automobile industry and the communities to solve the world’s transportation, environmental and employment challenges through the use of AI technology and localized smart transportation innovations. DiDi strives to create better life experiences and greater social value, by building a safe, inclusive and sustainable transportation and local services ecosystem for cities of the future.

For more information, please visit: www.didiglobal.com/news

#LI-Hybrid Team Overview

As a Senior Data Analyst at DiDi, you will operate as a problem solver within large-scale data ecosystems. You will be responsible for understanding upstream data pipelines, ensuring the reliability and performance of critical data systems, along with deep-diving into data issues.

You will be the bridge between Business Intelligence, Data Engineering, and global teams, taking full ownership of ambiguous problems: from detection to root cause analysis and resolution. This role is ideal for someone who thrives in complexity, is highly curious, and can independently navigate systems, teams, and data to drive impactful solutions across the organization.





This is a high-impact role for someone who is curious, persistent, and highly technical, with the ability to go deep, move fast, and solve problems that others can't.

Role Responsibilities

- End-to-End Problem Solving – Own complex data issues from start to finish: identify anomalies, investigate deeply across layers (BI, ETL, infrastructure), determine root cause, and deliver scalable solutions with minimal supervision.
- Deep Dive & Root Cause Analysis – Perform structured, hypothesis-driven deep dives into data discrepancies, performance issues, and system bottlenecks. You will be going beyond surface-level analysis into pipelines, transformations, and compute layers.
- Data Infrastructure & Performance Monitoring – Monitor and analyze data platform performance (query latency, queues, compute utilization such as vCores). Identify inefficiencies and propose optimizations or scaling strategies.
- Cross-Functional & Global Collaboration – Work closely with Data Engineering, Product, and global teams (across regions and time zones) to debug issues, align on data definitions, and ensure system reliability.
- Process Optimization & Automation – Continuously improve data workflows by optimizing queries, refining data models, and automating repetitive validation or monitoring tasks.




- Documentation & Knowledge Sharing – Create clear, structured documentation of data models, issues, root causes, and solutions to enable scalability and knowledge transfer across teams.
- Stakeholder Communication – Translate complex technical findings into clear, actionable insights. Set expectations, explain trade-offs (speed vs accuracy), and influence decision-making.

Role Qualifications

- Fluent in English (mandatory) and Spanish. Ability to communicate clearly with global teams and document findings in a structured, professional manner.
- Education: Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or a related field.
- Experience: 4+ years in Business Intelligence, Data Analytics, or Data Enablement roles, with strong exposure to data troubleshooting and deep dives.
- Problem Solving & Ownership: Proven ability to independently solve ambiguous, complex data problems end-to-end with minimal guidance.
- Technical Skills:
- Advanced SQL (query optimization, debugging complex transformations)
- Strong understanding of data pipelines (ETL/ELT) and data modeling
- Experience working with large-scale data warehouses (Hive, Spark, BigQuery, Snowflake)
- Familiarity with data performance concepts (query optimization, partitioning, compute resources, concurrency)
- Python or R for data validation, automation, or deeper analysis.

- Data Infrastructure Awareness: Ability to understand and troubleshoot issues related to compute resources, data queues, and system performance.
- Analytical Mindset: Strong ability to break down complex systems, identify root causes, and connect technical

📌 Senior Data Engineer (Ciudad de México)
🏢 Didi Global
📍 Ciudad de México