Tax Data Pipeline Engineer — Scale & Automate ETL

doordashmexico; Ciudad de México; On-site; MXN 600,000 - 900,000; Full time

Job summary

DoorDash is seeking an experienced Data Engineer to join the Tax Data Mart team in Mexico City. You will own data ingestion, transformation, and data quality, enabling analytics and decision-making for multiple business lines.

You will design scalable pipelines, optimize warehouse structures, and collaborate with Analytics, Data Science, and business partners. Strong SQL, Python, and modern data tools are essential.

Qualifications

• 5+ years of experience in a data engineering or similar role.
• Experience using DBT or a similar product to manage data flows.
• Experience using Claude, Cursor and similar AI tools.
• You are an expert at using SQL with Snowflake.
• Proficiency in Python for building data pipelines.
• Experience with modern data stack and orchestration tools such as Airflow, Spark, or Kafka.
• You have strong attention to detail and communication skills.
• You can manage multiple projects independently.

Responsibilities

• Design, build, and maintain scalable data pipelines (ETL/ELT).
• Develop and optimize data models and warehouse structures for analytics.
• Collaborate with Analytics, Data Science, and business partners on data sources.
• Document requirements and data tooling needs for new initiatives.
• Monitor data quality and observability; resolve data issues.
• Prepare documentation for data systems and processes.
• Share knowledge and best practices with engineers and analysts.

Skills

ETL/ELT pipelines

DBT

SQL

Python

Snowflake

Airflow

Kafka

AI tooling

Data modeling

Project management

Education

Computer Science or related degree

Tools

DBT

Snowflake

Airflow

Spark

Kafka

Claude

Cursor

Job description

DoorDash is seeking an experienced Data Engineer to join the Tax Data Mart team in Mexico City. You will own data ingestion, transformation, and data quality, enabling analytics and decision-making for multiple business lines.

You will design scalable pipelines, optimize warehouse structures, and collaborate with Analytics, Data Science, and business partners. Strong SQL, Python, and modern data tools are essential.