Data Engineer: Scale Pipelines & Reliability

DoorDash ANZ; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

DoorDash is seeking a Software Engineer, Data to scale data reliability, infrastructure, automation, and tools for a growing set of data needs. You will own critical data systems and help build scalable pipelines for data lake and warehouse environments.

You will design data models, improve ingestion and ETLs, and support multiple product teams. Strong programming, SQL, and distributed systems experience are essential in a fast-paced, collaborative setting.

Qualifications

• 3+ years of professional data engineering experience.
• Experience in data platform and data engineering or similar role.
• Proficiency in Python, Kotlin or Scala.
• 3+ years in ETL orchestration and workflow tools like Airflow.
• Strong SQL and distributed computing fundamentals.
• 3+ years with Spark/Presto and streaming tech (Kafka, Flink).
• Excellent communication with technical and non-technical teams.

Responsibilities

• Own critical data systems supporting multiple products/teams.
• Develop and enforce best practices for data infrastructure and automation.
• Design and implement large-scale data models and pipelines for Data Lake/Warehouse.
• Improve reliability and scalability of ingestion, ETLs, reporting tools.
• Manage a portfolio of data products delivering high-quality data.
• Onboard and support other engineers joining the team.

Skills

Python

SQL

Airflow

Spark

Kotlin

Scala

Presto

Kafka

Data pipelines

Tools

Airflow

Kafka

Spark

Presto

Job description

DoorDash is seeking a Software Engineer, Data to scale data reliability, infrastructure, automation, and tools for a growing set of data needs. You will own critical data systems and help build scalable pipelines for data lake and warehouse environments.

You will design data models, improve ingestion and ETLs, and support multiple product teams. Strong programming, SQL, and distributed systems experience are essential in a fast-paced, collaborative setting.