Faire
Senior Staff Data Engineer - Platform Data and Analytics
San Francisco, CA · Staff+
Sponsorship not specified$268k-$369kDetected 1 day ago
PythonSQLSnowflakeAWSMachine LearningSparkAirflowData EngineeringData ScienceLeadershipCommunicationMentoring
About the role
- The Platform group empowers teams across Faire: Product Engineering, Data Science, Product Management, Strategy, Analytics, Finance, etc.
- Enabling these functions to do their best work without concern about underlying infrastructure.
- We care about good engineering practices, excellent developer experience, security, testability, ease of maintenance, and scaling to serve millions of users.
Responsibilities
- lead architecture development with a focus on data integrity, security, performance, scale, reliability, monitoring/alerting.
- Including cost-efficient compute and storage, ETL and ELT pipelines, data ingest and warehousing, monitoring, alerting, and cost tracking for workloads, design practices for foundational assets, data security, privacy, and governance.
- Plan and execute on technology projects that scale with Faire's growth; lead architecture development with a focus on data integrity, security, performance, scale, reliability, monitoring/alerting.
- We enable product engineering teams to build and operate software with unmatched speed and quality.
- Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
Requirements
- Strong SQL and Python skills and experience.
- Experience in guiding technical and product teams on such trade-offs.
- Experience diagnosing, mitigating, and permanently addressing data system production issues at scale.
- Experience rolling out patterns for workload performance and cost optimization.
- Experience operating in a growth stage company, with a data function consisting of multiple teams and over 60 people.
- A bachelor's degree in Computer Science/Software Engineering or equivalent industry experience.
- Experience designing, developing, and operating data streaming, batch, ETL/ELT, orchestration, storage, compute, workflow systems at scale.
- Experience defining architecture patterns and building reliable, scalable data pipelines, orchestration of data movement, querying, transformation, and frameworks for analytics engineering workflows.
- Data warehousing experience at Petabyte scale.
- Snowflake, Airflow, Spark experience especially valuable.
- Understanding of performance, capacity, cost trade-offs in data processing systems. Experience in guiding technical and product teams on such trade-offs.
- Experience diagnosing, mitigating, and permanently addressing data system production issues at scale. Experience rolling out patterns for workload performance and cost optimization.
- Experience operating in a growth stage company, with a data function consisting of multiple teams and over 60 people. Enabling teams to operate in a self-serve model.
- Excellent communication, leadership, and influencing skills.
- Select technologies we use and teach: AWS, Snowflake, Airflow, Spark, Python, Kotlin.
Skills
- AWS, Snowflake, Airflow, Spark, Python, Kotlin.
- Faire is a technology wholesale platform built on the belief that the future is local.
- Salary Range
- Best in
Compensation
- San Francisco: the pay range for this role is $268,000 to $368,500 per year.
- Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location.
- The base pay range provided is subject to change and may be modified in the future.
- Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year.
- the pay range for this role is $268,000 to $368,500 per year.
Benefits
- This role will also be eligible for equity and benefits.
Company info
- This role is for a highly experienced technical leader in the data space, whose influence spans multiple Platform and Product groups.
This listing is sourced directly from Faire's careers page and normalized into a canonical job model.