Senior Data Engineer — Scalable Pipelines & Data Lake

DoorDash, Inc.; Seattle (WA); Hybrid; USD 144,000 - 212,000; Full time

Job summary

DoorDash is seeking a Senior Data Engineer to scale our data infrastructure and ETL pipelines across data lake and data warehouse workloads. This hybrid role is based near our engineering hubs in Seattle, Sunnyvale, or San Francisco.

You will partner with business and technical teams to define data requirements, design scalable data models, build robust pipelines, and implement data quality checks and monitoring.

Qualifications

• 5+ years of professional experience.
• 3+ years experience in data engineering, BI, or similar.
• Proficiency in Python/Java.
• 3+ years of experience with Airflow, Flink, Oozie, Azkaban on AWS/GCP.
• Expert in SQL and distributed computing.
• 3+ years with Spark, Hive, Druid, Presto and Kafka/Flink.
• Experience with Snowflake, Redshift, PostgreSQL.
• Excellent communication skills.
• Knowledge of Tableau, Superset, Looker.

Responsibilities

• Understand data requirements with business partners.
• Collaborate with engineering, product, and 3rd parties to collect data.
• Design, develop, and implement large-scale data models and pipelines for Data Lake and Data Warehouse.
• Develop data quality checks, QA, and monitoring routines.
• Improve reliability and scalability of ETL processes.
• Manage portfolio of data products delivering high-quality data.
• Onboard and support other engineers joining the team.

Skills

Python/Java

SQL

Data modeling

Data engineering

Communication

Tools

Airflow

Flink

Oozie

Azkaban

Snowflake

Redshift

PostgreSQL

Kafka

Spark

Hive

Presto

Job description

DoorDash is seeking a Senior Data Engineer to scale our data infrastructure and ETL pipelines across data lake and data warehouse workloads. This hybrid role is based near our engineering hubs in Seattle, Sunnyvale, or San Francisco.

You will partner with business and technical teams to define data requirements, design scalable data models, build robust pipelines, and implement data quality checks and monitoring.