Data Engineer II: Build Scalable Data Pipelines

Doordashusa; San Francisco (CA); On-site; USD 131,000 - 285,000; Full time

Benefits offered by this job

Medical insurance

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off and sick leave

Job summary

DoorDash is seeking a Software Engineer II for Data Engineering to scale data infrastructure, automation and tools to meet growing business needs. You will design large-scale data models and pipelines for data lake and data warehouse, and develop data quality checks with QA and monitoring.

You will collaborate with business partners and engineering teams to collect required data, improve ETL reliability, and manage a portfolio of data products delivering high-quality, trustworthy data.

Qualifications

• 3+ years of professional experience in data engineering or BI
• Experience with ETL orchestration and workflow management tools
• Proficiency in Python/Java
• Strong SQL and database fundamentals
• Experience with distributed data ecosystems (Spark, Hive, Druid, Presto) and streaming (Kafka/Flink)

Responsibilities

• Collaborate with partners to understand data requirements
• Design and implement large-scale data models and pipelines for data lake and data warehouse
• Develop data quality checks, QA, and monitoring
• Improve reliability and scalability of ETL processes
• Manage portfolio of data products delivering high-quality data
• Onboard and support engineers joining the team

Skills

Data engineering

Python/Java

SQL

ETL development

Data modeling

Airflow

Spark

Kafka

AWS/GCP

Distributed systems

Tools

Airflow

Flink

Oozie

Azkaban

Spark

Hive

Druid

Presto

Snowflake

Redshift

PostgreSQL

Kafka

Looker

Tableau

Superset

Job description

DoorDash is seeking a Software Engineer II for Data Engineering to scale data infrastructure, automation and tools to meet growing business needs. You will design large-scale data models and pipelines for data lake and data warehouse, and develop data quality checks with QA and monitoring.

You will collaborate with business partners and engineering teams to collect required data, improve ETL reliability, and manage a portfolio of data products delivering high-quality, trustworthy data.