Senior Tax Data Engineer & Pipelines Lead

DoorDash USA; Ciudad de México; On-site; MXN 600,000 - 1,200,000; Full time

Job summary

DoorDash is seeking an experienced Data Engineer to join the Tax Data Mart team. You will design and operate data pipelines and infrastructure powering analytics across the company.

Reporting to the Manager of Tax Data Mart, you will own ingestion, transformation, and data quality, collaborating with Analytics, Data Science, and business partners to enable accurate decision-making as the company scales. You will also design, implement, and operate data pipelines and related processes to support

Qualifications

• 5+ years of experience in data engineering or similar role.
• Proficient in SQL and Python for building data pipelines.
• Experience designing and maintaining scalable data systems.
• Familiarity with DBT, Snowflake, and modern data stack tools.
• Ability to collaborate with Analytics, Data Science, and business partners.

Responsibilities

• Design, build, and operate scalable data pipelines (ETL/ELT) powering analytics.
• Develop and optimize data models and data warehouses for reporting.
• Work with stakeholders to define data requirements and validate new data sources.
• Monitor data quality, reliability, and observability; resolve data issues.
• Document data systems and share best practices across the team.

Skills

SQL

Python

Data modeling

ETL/ELT pipelines

Analytics collaboration

AI tooling awareness

Education

Computer Science / IT / Engineering 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. You will design and operate data pipelines and infrastructure powering analytics across the company.

Reporting to the Manager of Tax Data Mart, you will own ingestion, transformation, and data quality, collaborating with Analytics, Data Science, and business partners to enable accurate decision-making as the company scales. You will also design, implement, and operate data pipelines and related processes to support