Data Engineer

Who we are

Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale.

Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term.

About the role:

Samsara is seeking a Data Engineer to join our Data team, comprising both Data Scientists, Data Analysts, and Data Engineers. Our team is at the forefront of enhancing data analysis efficiency and effectiveness. Our data engineers develop and maintain data pipelines integral to our analytics data model.

As a Data Engineer, your primary responsibility will be designing and maintaining data pipelines, primarily utilizing SparkSQL and Pyspark, within our central data lake. These pipelines are crucial for ingesting and transforming source data from our IOT devices and software products into our core data model, facilitating statistical analysis, model training, and dashboard creation.

This is a remote position open to candidates residing in the US. Relocation assistance will not be provided for this role.

You should apply if:

You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact – helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.

You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.

You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.

You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win – together.

In this role, you will:

Build and maintain highly reliable computed tables, incorporating data from various sources, including unstructured data like video and audio, Samsara sensor & product data, and customer metadata.

Access, manipulate, and integrate external datasets with internal data.

Deliver high-quality data with strong uptime and reliability requirements, including customer-facing data sets.

Collaborate closely with cross-functional teams such as Data Science & Analytics, AI/ML, and other Data Engineers to ensure high-quality data for diverse purposes from causal inference, model training, and dashboarding.

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.

Minimum requirements for the role:

BA / MS degree in Computer Science, Statistics, or a related discipline.

4+ years experience in a data engineering-focused role.

Demonstrated experience in designing data models at scale.

Proficiency in building ETL pipelines to handle large volumes of data.

Experience with Spark-based data platforms.