Data Platform Engineer: Scale & Real‑Time Analytics

DoorDash Mexico; San Francisco (CA); On-site; USD 131,000 - 192,000; Full time

Job summary

DoorDash is seeking a data platform leader to design and operate scalable data frameworks that power real-time personalization and analytics. You will influence core platform decisions and shape the roadmap for a rapidly growing business in a data-driven environment.

You will drive reliability, scalability, and cost efficiency across data ingestion, processing, and governance, while partnering with engineers, analysts, and ML practitioners to enable trusted insights.

Qualifications

• BS/MS/PhD in Computer Science or equivalent.
• 2+ years of industry experience in software engineering.
• Proficiency with AI coding tools (e.g., Claude Code, Codex, Cursor) across the full SDLC.
• Experience with big data domains (streaming, batch, data lake OLAP) and real-time analytics at scale (Kafka, Flink, Spark...).
• Open-source data infrastructure experience with tools like DataHub, Spark, Airflow, Kafka, Flink is a plus.

Responsibilities

• Set vision and strategy for the frameworks charter and align with business growth.
• Scale the analytical platform to handle growing data and use cases.
• Build and operate high-scale, reliable, cost-efficient data infrastructure.
• Collaborate with stakeholders to deliver platform solutions.
• Mentor and uplift teammates to foster a positive culture.

Skills

Software engineering

Big data concepts

AI coding tools

Education

BS/MS/PhD in Computer Science

Tools

Kafka

Flink

Spark

Cassandra

ClickHouse

Iceberg

Airflow

DataHub

Job description

DoorDash is seeking a data platform leader to design and operate scalable data frameworks that power real-time personalization and analytics. You will influence core platform decisions and shape the roadmap for a rapidly growing business in a data-driven environment.

You will drive reliability, scalability, and cost efficiency across data ingestion, processing, and governance, while partnering with engineers, analysts, and ML practitioners to enable trusted insights.