• Architect and maintain complex marketing databases, datasets, pipelines and Samsara’s Customer Data Platform (CDP) to enable advanced segmentation, targeting, automation and analytics. • Design and implement data infrastructure for AI/ML initiatives, including building pipelines for Generative AI applications, feature stores, and vector database integrations to support predictive modeling and personalization. • Support the execution of expanding conversational BI and Ambient AI within the marketing organization in partnership with the BI team. • Support in the automation of many manual tasks through the use of AI leading to efficiency gains for the whole marketing organization. • Manage critical, high-volume data pipelines to enable our growth initiatives and advanced analytics. Manage the SLAs for those data pipelines and constantly improve efficiency and data quality. • Facilitate sophisticated data integration and transformation requirements for moving data between applications; ensuring interoperability of applications with data mart, AI models, and CDP environments. • Autonomously partner directly with non-technical stakeholders (Marketing, Sales, Ops) to translate ambiguous business questions into technical requirements and scalable data solutions without needing constant supervision. • Write sophisticated yet optimized data transformations in Python/SQL to generate data products consumed by customer systems and Analytics, Marketing Operations, and Sales Operations teams. • Mentor junior engineers, conduct code reviews, and help define best practices for the data engineering team.

• Develop, maintain, and enhance production ETL pipelines using Informatica (PowerCenter and/or IICS) • Write, optimize, and troubleshoot complex SQL used for transformations, validations, and analytics • Support and enhance data pipelines loading into Snowflake • Debug failed ETL jobs, data discrepancies, and performance issues across ETL and warehouse layers • Collaborate with business and technical stakeholders to understand data requirements and translate them into working solutions • Support data warehouse environments across cloud and hybrid architectures • Maintain technical documentation, mappings, and workflow logic • Participate in SDLC processes including deployments, testing, and version control

• Design and build scalable data pipelines that ingest, process, and transform high-volume event streams and historical data • Develop and maintain APIs that deliver analytics, trend reports, and drill-down capabilities to internal teams and external customers • Build robust infrastructure for data quality monitoring, ensuring accuracy and completeness across customer and artifact datasets • Optimize data storage and query performance using systems like ClickHouse, Kafka, NATS, and PostgreSQL to support real-time and batch use cases • Implement usage tracking, auditing, and event processing systems that provide visibility into platform behavior • Create reliable data ingestion systems for security scan results, SBOM data, and artifact metadata • Build infrastructure for outbound integrations that deliver Socket data to customer systems • Collaborate with product, security research, and engineering teams to understand data needs and deliver solutions that scale

• Create real-time streaming data solutions. • Implement large scale, high-performance data analytics, and data integration platforms. • Design and develop ETL solutions. • Design and develop frameworks and other approaches for rapid development of high-quality data and reporting solutions. • Perform complex data analysis to identify trends, patterns, and opportunities. • Design, develop, and support database applications spanning multiple lines of business. • Consult on database design and code reviews across the organization as needed. • Evaluate performance and perform appropriate troubleshooting as necessary. • Work with business users to understand project requirements and resolve issues. • Craft and maintain technical documents including educational materials, standards, and best practices. • Participate in on-call rotation for off-hours support. • Collaborate effectively across various departments and teams to implement solutions. • Collaborate on the product roadmap for data engineering to define capabilities of the platforms and services and delivery timelines.