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Tech Lead Manager (Data Engineer)
Waymo
- San Francisco, California, United States
- San Francisco, California, United States
About
BS/MS/PhD in Computer Science or a related field
7+ years of experience in data engineering, software engineering, or a related role, with a focus on large-scale data systems
Proven experience (2+ years) in managing and leading engineering teams, including hiring, coaching, and performance management
Demonstrated technical leadership in designing, architecting, and delivering complex, large-scale data systems and pipelines, including setting technical direction, making key design trade-offs, and mentoring other engineers
Strong understanding of data warehousing concepts, data modeling, and ETL/ELT processes
Expertise in SQL and proficiency in at least one programming language such as Python, Java, or C++
(Desirable) Experience building and scaling data lakes or large-scale data platforms
(Desirable) Expertise with big data technologies and distributed systems (e.g., Spark, Flume, Kafka, BigQuery, etc.)
(Desirable) Knowledge of data governance, data quality frameworks, and data security best practices
(Desirable) Experience working in a fast-paced, product-driven environment
(Desirable) Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams
(Desirable) Domain knowledge in ride-hailing, mobility, or autonomous vehicles
What the job involves
This role follows a hybrid work schedule and reports to Sr. Staff TLM
Lead, mentor, and grow a team of data engineers responsible for the Waymo Commercialization Data Lake and associated data pipelines
Define and drive the technical vision, strategy, and roadmap for the Data Engineering team, aligning with Waymo's goals
Oversee the design, development, and operation of scalable and reliable data pipelines, data stores, and data models to ingest, process, and serve commercialization data
Establish and enforce best practices for data quality, governance, security, and performance optimization within the data ecosystem
Collaborate closely with Product Data Scientists, Engineering, and Operations teams to understand data needs, define requirements, and deliver robust data solutions
Foster a culture of engineering excellence, innovation, and continuous improvement within the team
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Languages
- English
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