Data Pipeline Engineer — AI-Driven ETL & Analytics

Doordash-Mexico; Ciudad de México; On-site; MXN 800,000 - 1,200,000; Full time

Job summary

DoorDash seeks an experienced Data Engineer to join the Tax Data Mart team in Mexico City. You will own ingestion, transformation, and data controls to empower analytics and decision making. You will design, implement, and operate data pipelines and related processes to support the Company’s growth.

You will collaborate with Analytics, Data Science, and business partners to develop new data sources, ensure data quality, and document systems and processes.

Qualifications

• 5+ years of experience in a data engineer or similar role.
• Experience with DBT or a similar data flow management tool.
• Experience using Claude, Cursor and AI tooling.
• Strong SQL skills and Snowflake experience.
• Python proficiency for building data pipelines.
• Experience with modern data stack and orchestration tools.

Responsibilities

• Design, build, and maintain scalable data pipelines that serve data across the company.
• Develop and optimize data models and warehouse structures for analytics.
• Collaborate with Analytics, Data Science, and business partners on new data sources.
• Identify and document requirements to support new initiatives and system improvements.
• Leverage AI-assisted tooling to automate recurring data tasks and pipelines.
• Monitor data quality, reliability, and observability, resolving data issues.

Skills

SQL

Python

Data modeling

ETL/ELT

Data quality

Education

Bachelor's degree in Computer Science / IT / Engineering

Tools

DBT

Snowflake

Airflow

Spark

Kafka

Job description

DoorDash seeks an experienced Data Engineer to join the Tax Data Mart team in Mexico City. You will own ingestion, transformation, and data controls to empower analytics and decision making. You will design, implement, and operate data pipelines and related processes to support the Company’s growth.

You will collaborate with Analytics, Data Science, and business partners to develop new data sources, ensure data quality, and document systems and processes.