Serve as a primary responder for production data incidents, quickly diagnosing root causes, implementing fixes, and ensuring data integrity.
Design, implement, and maintain monitoring, logging, and alerting systems for all production data pipelines and infrastructure.
Manage, deploy, and maintain data and integrations pipelines and APIs.
Continuously identify and implement optimizations to improve the speed, scalability, and efficiency of data processing jobs and API performance.
Develop and enforce data validation and quality checks within the pipelines to minimize errors and inconsistencies in production data.
Collaborate with DevOps teams on managing the underlying infrastructure (AWS components) that hosts the data platform.
Maintain comprehensive and up-to-date documentation for all operational procedures, pipeline architectures, and troubleshooting runbooks.
Communicate incident status and SLA reports to management.
Develop & deploy data pipelines, backend ingestion or integration jobs to support minor enhancements and bug fixes.
Work with data from a variety of sources including but not limited to: CRM data, Product data, Marketing data, Order flow data, Support ticket volume data, Finance data etc.
Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices.

A Bachelor’s degree in computer science, data engineering, data science, information technology, or equivalent engineering program.
5+ years of experience in a Data Engineering, Data Operations, or SRE role supporting production data environments & user support on data issues.
Must have SQL experience to perform data analysis.
Experience with Python or similar scripting language.
Exposure to ETL tools such as Fivetran, DBT, Workato or equivalent.
Exposure to python based API frameworks, API management tools.
RDBMS: MySQL, AWS RDS/Aurora MySQL, PostgreSQL, Oracle or equivalent.
Experience with at least one major cloud provider (AWS, GCP, or Azure).
Data warehouse: Databricks, Google Big Query, AWS Redshift, Snowflake or equivalent.
CI/CD, DevOps tools and practices.

Senior Data Ops Engineer

Key skills