Engineering Manager, Data

DoorDash is a data driven organization and relies on timely, accurate and reliable data to drive many business and product decisions. Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing data structures and data warehouse architecture, this team serves as the foundation for decision-making at DoorDash. The focus extends to enhancing the developer experience by creating tools that support the organization's high-velocity demands.

To lead the growing team of Data engineers we are looking for managers who are passionate about Data and are thought leaders in coaching, guiding and leading teams to make Data a winning edge for DoorDash.

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

DoorDash is looking for a Data Engineering Manager to guide the development of enterprise-scale data solutions. This manager will also act as a technical expert on all things related to data architecture to empower the greater community of data engineers, data scientists, and DoorDash partners. Your focus extends to fostering an engineering culture of excellence, empowering engineers to deliver reliable, flexible solutions at scale. Additionally, you'll play a pivotal role in building and nurturing a top-performing team, driving innovation and success in a dynamic, fast-paced environment. You must be located in San Francisco, CA, Sunnyvale, CA, or Seattle, WA for this hybrid position.

• You are a people leader. You thrive in hiring, building, growing and nurturing impactful business focused data teams
• You are a technology leader. You drive the technical and strategic vision for the embedded pods and foundational enablers to meet current and future needs for scale and interoperability
• You strive for continuous improvement of data architecture and development process
• You think of quick wins while planning for long term strategy and engineering excellence. You are excited about breaking down large systems into easy to use data assets and reusable components
• You are excited about cross collaboration with stakeholders, external partners and peer data leaders
• You are a planner and executioner. You know the tools to plan for short term and long term team and stakeholder success
• You think of reliability and quality as must have

We’re hiring a Data Solutions Engineer with deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions. In this role, you will design, optimize, and scale distributed data access layers that power DoorDash’s most critical systems, ensuring high availability, low latency, and fault tolerance.

You’ll serve as a hands-on architect and technical partner to product engineering and infrastructure teams, helping translate complex business requirements into resilient and scalable data models. Your work will directly influence the evolution of Taulu, DoorDash’s unified storage abstraction layer, by shaping best practices and identifying platform gaps through real world engagements.

This is a high-impact, cross functional role that combines deep technical expertise with a customer centric approach. You’ll lead solutioning engagements from design through production, drive the adoption of Taulu modeling best practices, and ensure that our systems meet goals around reliability, cost efficiency, and velocity. You must be located in San Francisco, Sunnyvale, Seattle or New York for this hybrid opportunity. 

• Design and implement highly scalable, fault tolerant distributed database solutions using Taulu, Apache Cassandra, Redis, Kafka, and other paved path storage solutions. 
• Architect and optimize multi-region, globally distributed systems to meet our high standards for availability, latency, and throughput.
• Lead data modeling, performance tuning, and capacity planning for large-scale, mission-critical storage workloads.
• Partner with product engineering and infrastructure teams to deeply understand domain specific data needs and guide them in adopting paved path storage solutions.
• Serve as the DRI for solutioning engagements, owning modeling in Taulu from experimentation through launch and scale.
• Shape the evolution of Taulu by identifying abstraction gaps and converting customer feedback into platform improvements.
• Apply workload-aware design patterns, including caching strategies, partitioning, and consistency tuning to improve performance and efficiency.
• Drive adoption of operational best practices across observability, schema design, capacity planning, and cost optimization across storage systems.
• Promote clarity and continuity by contributing to solutioning playbooks, decision logs, and architectural documentation.

• B.S., M.S., or PhD. in Computer Science or equivalent
• 10+ years of experience working in data engineering or a related domain
• 2+ years of hands-on management experience
• Experience hiring and growing teams
• Exceptional communication and leadership skills, with a proven ability to operate in a fast moving environment
• Experienced with performance management, coaching, mentoring and growing teams
• Hands-on approach to closing gaps in data infrastructure and technical execution, able to code in SQL and Python
• Prior experience with Snowflake/Redshift, AWS/GCP, Hadoop/Spark/Big data, Lambda/KAPPA architectures, Flink/Airflow 
• Prior experience with large scale batch/real time ETL orchestration
• Prior experience in Systems Engineering - you've built  meaningful big data processing systems at scale, and experience with big data compute engines such as Apache Spark and Apache Flink
• Familiarity with Datalake solutions such as Delta Lake, Apache Iceberg
• Familiarity with a cloud based environment such as AWS
• Experience with these specific technologies is not required but helpful
• Building systems directly powering online applications
• Exposure to various databases such as CockroachDB, Cassandra, and PostgreSQL

• You have 10+ years of experience designing and scaling distributed data systems, with deep expertise in NoSQL technologies like Apache Cassandra, DynamoDB, or ScyllaDB.
• You have a strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.
• You’ve led data modeling efforts for high-throughput, low-latency workloads and understand the real-world trade-offs involved in NoSQL schema design.
• You are experienced with caching technologies like Redis or Memcached and know how to layer them effectively over storage systems to optimize for performance and cost.
• You have a customer-first mindset, and thrive when working closely with product and platform teams to translate complex requirements into clean, scalable data models.
• You are skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.
• You have a track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.
• You document decisions, share learnings, and take pride in contributing to reusable playbooks and durable frameworks for others to build upon.
• Bonus: You’ve worked on or contributed to open-source distributed databases.  

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