Drive vision & strategy for building the Core Data Platform charter and position it to handle the challenges of a rapidly growing business
You will bring your expertise in building and operating high-scale systems with a focus on the reliability and quality of analyzed data
Lead a well-run, successful team via coaching, mentoring, and providing technical and career guidance
Build, sustain, and grow a diverse team to address the growing needs of the organization
Build data-intensive solutions that are used by DoorDash engineers, data scientists, analysts, or business users from across the company. Drive and deliver the ongoing product vision of data products
Think in terms of building data products and not systems. You excel at driving the engineering vision, strategy, and execution for an organization consisting of multiple teams and sub-teams
You are a technology leader. You excel at mentoring and guiding a fast-growing organization in setting the right architectural patterns, handling build vs buy decisions, working with various vendors in the data solutions space, making judicious investments in the right areas anticipating what the company needs a few years down the road
You think of quick wins while planning for long-term strategy and engineering excellence. You are excited about breaking down large systems into manageable, sustainable components that can be iterated on
You strive for continuous improvement of data architecture and development process
You are excited about cross-collaboration with stakeholders, external partners, and peer data leaders
You love rolling up your sleeves to get down to the lowest level of detail
Foster a positive and supportive work culture
Requirements & Skills:
B.S., M.S., or PhD. in Computer Science or equivalent
10+ years of industry experience
5+ years of experience in an engineering management role
You have extensive experience building and operating scalable, fault-tolerant, distributed systems in the area of large-scale data-intensive applications
You have experience with a range of large-scale ((multiple PetaBytes) data systems such as data processing, complex/high volume real-time insights, data quality and reliability frameworks, cost efficiency, etc. The following areas are representative of the breadth of data technologies in which familiarity would be optimal (Experience with each of these specific technologies or similar alternatives is not required but is helpful)
Apache Spark, Airflow, Trino, Pinot, Flink, Kafka
Data Warehousing and Data Lake technologies such as Snowflake, Databricks, various table formats such as Iceberg or Delta Lake
