• Own the design and organization of the program’s data lake, including schema definitions, partitioning strategy and metadata indexing. • Design and maintain end-to-end pipelines that ingest high-bandwidth sensor logs from vehicles into cloud storage with high reliability and tolerant of ad-hoc and intermittent connectivity mechanisms. • Develop data validation and integrity checks that can detect corrupted information, missing sensors, and inconsistent calibration prior to the data being processed by downstream systems. • Implement retention, tiering and lifecycle policies for data to balance storage costs with development value. • Build tooling to query raw logs to produce curated training and evaluation datasets. • Build automation to run cost-effective pseudo-labeling workflows at the scale of data ingest. • Implement data quality and model performance metrics that are used to direct labeling effort toward the highest-value examples. • Deploy and maintain data visualization tooling to support log review, annotation QA, and autonomy debugging workflows. • Build integrations between the visualization tooling and the data lake so engineers can navigate from a dataset entry or model failure directly to the origin log data • Work with autonomy engineers to define and surface custom visualization panels and implement metrics for analyzing unstructured operating environments. • Build dashboards that provide the autonomy engineers visibility into data coverage by terrain type, operating environment and geographic region. • Establish and document data contracts between the data services and model training consumers. • Partner with perception, planning and embedded engineers across the data lifecycle: from shaping the logging schemas and collection triggers to defining the dataset interfaces that supply model training and evaluation. • Define data engineering standards, best practices, and tooling choices for an innovative and fast-paced team. • Contribute to the data roadmap and provide input to technical leadership on investment priorities. • Mentor junior engineers and raise the team’s capabilities in data infrastructure scalability and operational hygiene.

• Apoiar o desenvolvimento de soluções que utilizam IA/ML, como APIs inteligentes e automações. • Implementar e manter pipelines de dados para treinamento e consumo de modelos. • Auxiliar na integração de modelos de IA em aplicações e serviços. • Colaborar na construção de componentes reutilizáveis (APIs, serviços, bibliotecas internas). • Apoiar a automação de processos utilizando IA (ex: classificação, recomendação, análise de texto). • Trabalhar em conjunto com times de dados, MLOps e engenharia. • Contribuir com melhorias contínuas em performance, qualidade e confiabilidade das soluções. • Apoiar na monitoração básica de aplicações e resolução de incidentes.

• Entwicklung und Betrieb von Datenpipelines und ETL-/ELT-Prozessen • Integration und Verarbeitung interner und externer Datenquellen • Nutzung von KI-gestützten Entwicklungstools • Sicherstellung des Betriebs der Datenplattform inklusive Monitoring und Fehleranalyse • Umsetzung technischer Anforderungen und Tickets • Analyse und Umsetzung fachlicher Anforderungen gemeinsam mit den Fachbereichen • Optimierung bestehender Datenprozesse und Datenlogiken • Durchführung von Deployments sowie Pflege technischer Dokumentationen • Mitarbeit bei Tests, Releases sowie der Einhaltung von Daten-, Sicherheits- und Governance-Vorgaben • Analyse und Lösung technischer Probleme im Datenplattformbetrieb

• Design, build, and operate high-scale data ingestion and replication systems from Samsara’s primary production data stores, including RDS, DynamoDB, internal APIs, and event-driven systems, into our data lakehouse. • Build and maintain reliable, scalable, and modern data platform infrastructure capable of handling petabytes of data across Samsara’s analytics, AI, product, and operational use cases. • Improve the reliability, observability, scalability, security, and developer experience of Samsara’s Spark and Databricks-based data processing platform. • Develop internal libraries, APIs, frameworks, and tooling in languages such as Go and Python to help teams across Samsara move, process, discover, and access data safely and efficiently. • Work on foundational data lake and lakehouse technologies, including Delta Lake on S3, data catalogs, metadata services, orchestration systems, and platform automation. • Collaborate closely with infrastructure, product engineering, data science, analytics, security, and data engineering teams to understand platform needs and deliver durable, scalable solutions. • Stay connected to modern data platform technologies and help shape Samsara’s long-term data infrastructure roadmap, including support for AI, privacy, security, global scale, and customer-facing data products. • Champion, role model, and embed Samsara’s cultural principles as we scale globally and across new offices.