Staff Software Engineer, Data Engineering

DoorDash's data engineering team builds the database solutions behind reporting, product analytics, marketing optimisation and financial reporting, which the posting describes as the foundation for decision making across the company. This Staff role is explicitly a technical lead position: architecting and scaling data reliability, infrastructure, automation and tooling as those needs grow. You would own critical data systems that support multiple products and teams, develop and enforce the best practices other engineers follow for data infrastructure, and improve the reliability and scalability of ingestion, data processing, ETL, reporting tools and the wider data ecosystem services. It is worth reading the experience requirement carefully, because it is layered rather than a single number: eight or more years overall as a hands on engineer and technical leader running multiple projects, of which six or more must be in data platform or data engineering specifically, plus four or more years each with orchestration tooling and the distributed data ecosystem. The real bar is higher than the headline figure suggests.

What the posting requires

- 8+ years of professional experience as a hands on engineer and technical leader, leading multiple projects
- 6+ years working specifically in data platform and data engineering or a similar role
- Proficiency in Python, Kotlin or Scala
- 4+ years with ETL orchestration and workflow management tools such as Airflow
- 4+ years with the distributed data ecosystem such as Spark and Presto, and streaming technologies such as Kafka, Flink or Spark Streaming
- Expert level database fundamentals, SQL, data reliability practices and distributed computing
- Proficiency using AI coding tools such as Claude Code, Codex or Cursor across the full development lifecycle, which DoorDash asks for explicitly
- Excellent communication with both technical and non technical teams, and knowledge of reporting tools

What you would actually be doing

- Acting as technical lead, architecting and scaling data reliability, infrastructure, automation and tooling
- Owning critical data systems that support multiple products and teams
- Developing, implementing and enforcing best practices for data infrastructure
- Improving the reliability and scalability of ingestion, data processing, ETL, reporting tools and data ecosystem services

Location and working pattern

Hyderabad.

A good fit if

You have led data platform work before and want ownership of systems the business genuinely depends on, including financial reporting where correctness is not negotiable.

Think twice if

You are still early in a data engineering career, or your data work has been alongside another specialism rather than the main thing. The stacked requirements, 8 years overall and 6 in data, mean a generalist backend engineer with some pipeline experience is unlikely to clear the bar.