Senior Data Engineer

DiDi; Monterrey; Presencial; MXN 600.000 - 800.000; Jornada completa

Descripción de la vacante

DiDi in Monterrey, Nuevo León is seeking a Senior Data Analyst to tackle complex data issues in large-scale environments. The ideal candidate will take on end-to-end data problem-solving, from anomaly detection to delivering scalable solutions, while collaborating closely with cross-functional teams.

The position requires fluency in English and Spanish, a bachelor's degree in a relevant field, and at least 4 years of experience in data analytics. This role promotes a culture of collaboration and diversity.

Formación

• 4+ years in Business Intelligence, Data Analytics, or Data Enablement roles.
• Proven ability to independently solve ambiguous, complex data problems.
• Strong experience with data troubleshooting and deep dives.

Responsabilidades

• Identify anomalies, investigate deeply, and deliver scalable solutions.
• Perform structured hypotheses-driven deep dives into data discrepancies.
• Monitor and analyze data platform performance and propose optimizations.
• Collaborate with Data Engineering and Product teams to debug issues.
• Continuously improve data workflows and automate repetitive tasks.
• Create clear documentation of data models and solutions.
• Translate complex technical findings into actionable insights.

Conocimientos

Fluent in English

Fluent in Spanish

Advanced SQL

Data pipeline understanding

Analytical mindset

Problem-solving

Educación

Bachelor’s degree in Engineering, Computer Science, Mathematics, Statistics, or related field

Herramientas

Python

R

Hive

Spark

BigQuery

Snowflake

Descripción del empleo

Senior Data Analyst

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, and 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.

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, 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 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.

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 issues to business impact.
• Stakeholder management: experience working with cross‑functional and international teams, aligning on priorities and driving resolution.
• Adaptability: comfortable operating in a fast‑paced, high‑pressure environment with evolving priorities and incomplete information.

Nice to have

• Experience collaborating closely with data engineering teams or contributing to pipeline design and debugging.
• Familiarity with monitoring tools, logging systems, or performance tracking frameworks.
• Experience with data management or governance tools (Collibra, Alation, Atlan).

Benefits & Culture

DiDi is an Equal Opportunity Employer that promotes equal opportunities to all candidates and employees. We value integrity, growth, win‑win collaboration, and diversity & inclusion, and we continually strive to create safe, pleasant and efficient user experiences while keeping data‑driven decisions at the core of our strategy.