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About This Role
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
Marketing Data and Analytics (MDA) is an integral team within Marketing. Our mission is to strengthen revenue performance by providing marketing and sales teams with the insights, tools, infrastructure, and consultation to make data\-driven decisions. This role sits on MDA's Engineering team, working alongside our BI and Data Science teams. We are a scrappy, fast\-moving team that takes ambiguous business problems and turns them into data products, AI agents, and automation, all while architecting and maintaining the data infrastructure that powers Samsara's marketing. This role is for someone who thinks on their feet, moves fast, and is comfortable owning problems end to end.
This role is open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.
### You should apply if:
- You want to impact the industries that run our world: Your efforts will result in real\-world impact, helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
- You are the architect of your own career: If you put in the work, this role won't be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.
- You're energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
- You want to be with the best: At Samsara, we win together, celebrate together and support each other. You will be surrounded by a high\-calibre team that will encourage you to do your best.
### In this role, you will:
- Design, build, and operate production AI systems, including agent orchestration, tool and API integrations, retrieval pipelines, and the evaluation harnesses that keep them reliable and trustworthy.
- Architect and maintain marketing databases, datasets, pipelines, and Samsara's Customer Data Platform (CDP) to enable advanced segmentation, targeting, automation, and analytics.
- Partner with the BI team to expand conversational analytics across the marketing organization.
- Identify and automate manual workflows with AI, taking ideas from concept to prototype to a credible path to production, and delivering efficiency gains across marketing and go\-to\-market teams.
- Stand up new data pipelines end to end, often for a tool that was onboarded yesterday: discovering the schema, working with partners inside Samsara and at the vendor, getting the data processed, and integrating it into our downstream systems.
- Own the reliability and data quality of what you build.
- Autonomously partner with technical and non\-technical stakeholders (Marketing, Sales, R\&D, and more) to translate ambiguous business questions into technical requirements and scalable solutions, without dedicated PM support.
- Ship high\-quality Python and SQL, increasingly by directing agentic coding tools, while holding a high bar for reviewing and verifying AI\-generated work before it reaches production.
- Mentor engineers, conduct code reviews, and help define best practices for the team.
### Minimum requirements for the role:
- 5\+ years of working experience in a data engineering or AI engineering role, including meaningful hands\-on data engineering experience.
- Expert Python and SQL knowledge with strong hands\-on data modeling experience.
- You have built and shipped systems that use LLMs or agents in production as part of your job.
- Agentic coding tools (e.g., Claude Code, Cursor) are part of your regular workflow. You actively seek out new ways to use AI to accelerate your work, and you verify and take ownership of AI\-generated output.
- Deep experience with data warehouse architectures, ETL/ELT, and the modern data stack (e.g., Databricks, DBT, Snowflake, BigQuery, or similar).
- Demonstrated ability to lead requirements gathering independently, bridging the gap between business needs and technical implementation, and to spot opportunities for automation through exploratory conversations with stakeholders.
- A self\-starter who performs well independently and as a team member, with strong communication and project management skills across technical and non\-technical audiences.
### An ideal candidate also has:
- A software engineering background beyond data or AI engineering, and a track record of taking ambiguous, unfamiliar problems and turning them into well\-architected, delivered solutions.
- Experience directly supporting a business function such as marketing, sales, finance, or business operations.
- Experience working with Databricks.
- Familiarity with go\-to\-market data and systems: CRM (e.g., Salesforce), marketing automation, web analytics, and CDPs (e.g., Hightouch, Segment).
- Experience evaluating and monitoring LLM systems in production.
Total Rewards
At Samsara, we build for the people who keep the global economy moving. We want owners, not passengers, which is why our rewards are designed to fuel high\-impact builders. Our compensation program delivers above\-market total compensation through a combination of base salary, performance\-based bonus/variable pay, and equity (for eligible roles) in a high\-growth public company. We meaningfully differentiate pay for our top performers, who have the opportunity to earn above\-market compensation that can outpace the broader market over time.
Beyond compensation, we provide the foundations that enable long\-term success: a flexible, employee\-led remote model, a professional development stipend, comprehensive health and parental leave plans, and more. If you're ready to build for the long term and own the outcome, your journey starts here.
Flexible Working
At Samsara, we embrace a flexible working model that caters to the diverse needs of our teams. Our offices are open for those who prefer to work in\-person and we also support remote work where it aligns with our operational requirements. For certain positions, being close to one of our offices or within a specific geographic area is important to facilitate collaboration, access to resources, or alignment with our service regions. In these cases, the job description will clearly indicate any working location requirements. Our goal is to ensure that all members of our team can contribute effectively, whether they are working on\-site, in a hybrid model, or fully remotely. All offers of employment are contingent upon an individual's ability to secure and maintain the legal right to work at the company and in the specified work location, if applicable.
Belonging at Samsara
At Samsara, we welcome everyone regardless of their background. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, protected veteran status, disability, age, and other characteristics protected by law. We depend on the unique approaches of our team members to help us solve complex problems and want to ensure that Samsara is a place where people from all backgrounds can make an impact.
Accommodations
Samsara is an inclusive work environment, and we are committed to ensuring equal opportunity in employment for qualified persons with disabilities. Please email [email protected] or click here if you require any reasonable accommodations throughout the recruiting process.
Our Commitment to Authenticity
We use Tofu, a fraud detection tool, to validate the authenticity of applications and protect against identity fraud. This ensures we are connecting with real people and allows us to prioritize genuine candidates. Please see Samsara's Candidate Privacy Notice for more information.
Fraudulent Employment Offers
Samsara is aware of scams involving fake job interviews and offers. Please know we do not charge fees to applicants at any stage of the hiring process. Official communication about your application will only come from emails ending in @samsara.com, @us\-greenhouse\-mail.io or @mail3\.guide.co. For more information regarding fraudulent employment offers, please visit our blog post here.
Salary Context
This $118K-$178K range is below the median for Data Engineer roles in our dataset (median: $153K across 35 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Samsara, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $185,000 based on 83 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($148K) sits 20% below the category median. Disclosed range: $118K to $178K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Samsara AI Hiring
Samsara has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect. Positions span San Francisco, CA, US, Chicago, IL, US, Atlanta, GA, US. Compensation range: $176K - $246K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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