Role Description We are looking for a highly technical, hands-on Senior Data Engineer to drive the evolution of our data delivery platform while leading a team of data engineers. This is a player-coach role: - Architect our next-generation data stack. - Build proofs-of-concept (POCs) and prove out new technologies before committing to them at scale. - Manage and grow a team of engineers who report directly to you. - Partner closely with engineering leadership to shape the technical direction of our pipelines while staying hands-on in the systems. We are deliberately moving away from a locked-in, vendor-heavy stack toward a flexible, largely open-source architecture that keeps our options open. We expect our engineers to work in a modern, AI-assisted way: - Use AI coding tools and prompt-based workflows to move faster without compromising quality. - Evaluate tools, build POCs, and make pragmatic, evidence-based decisions about what we adopt next. This is a build-and-prove role: - Write code, design schemas, profile queries, and understand the "how" and "why" behind every pipeline. - Mentor your team to do the same. - Team Leadership & People Management: Lead, manage, and grow a distributed team of data engineers (across North America and India). - AI-Assisted Development: Champion the use of AI coding assistants and prompt-based development. - Hands-On POCs & Prototyping: Build proofs-of-concept to validate new tools and patterns. - Tech Stack Strategy & Architecture: Help shape the technical vision for our ETL and data platform. - Platform Modernization: Drive the migration away from Azure Databricks toward a more open, flexible stack. - Build & Own Pipelines: Design, build, and optimize robust ETL pipelines. - Deep System Integration: Master all systems upstream and downstream to ensure seamless data delivery. - Operational Excellence: Establish testing frameworks, QA plans, observability, and CI/CD practices. - Technical Leadership Through Influence: Set technical standards and raise the bar across a distributed team. - Domain Mastery: Rapidly learn the nuances of the Advertising and Market Research domain. Qualifications - 8+ years in data engineering / ETL, with a strong track record as a hands-on engineer. - Hands-on experience using AI coding tools and strong prompt-based development skills. - Expert-level SQL and RDBMS skills with deep experience in SQL Server and Postgres. - Hands-on experience with technologies such as ClickHouse, dbt, and open-source data pipeline tools. - Strong, hands-on AWS experience. - Demonstrated ability to independently prototype, benchmark, and evaluate new technologies. - Strong proficiency in Python (or similar) for building and automating data pipelines. - Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Requirements - Previous experience in Advertising, Media, or Market Research (Bonus Points). - Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (Bonus Points). - Experience with streaming/real-time data technologies (e.g., Kafka) (Bonus Points). Benefits - Medical, Dental & Vision Insurance - 401k with Company Match - Flexible PTO - Commuter Benefits - Gym Discounts - Summer Fridays

• 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.

• Design, build, and operate scalable data pipelines, clean room environments, identity workflows, and privacy-safe data integrations that support NBCUniversal’s data collaboration ecosystem • Support partner onboarding into clean room environments across platforms • Configure and manage clean room environments, including data access, environment setup, platform configuration, and release validation • Implement privacy-preserving controls such as aggregation thresholds, anonymization techniques, approved query patterns, and output validation checks • Deploy and manage Python-based libraries, templates, and reusable components within the clean room and data platform ecosystem • Design, implement, and enforce granular role-based access control policies across data platform environments • Design, build, and operate scalable ELT pipelines using advanced SQL, Snowpark, PySpark, dbt, or similar technologies • Implement and evolve identity resolution logic that maps internal NBCU data to third-party identifiers

• Build and own the platform backbone for governed reporting and trusted AI workflows. • Make strategic architecture calls and build, harden, or rebuild pipelines. • Partner closely with Analytics, Engineering, and vendors to turn fragmented source systems into trustworthy data products. • Ensure our data infrastructure integrates securely and reliably with core operational systems. • Handle requirements, source profiling, ingestion design, QA, documentation, and support for data integrations. • Manage dependencies, retries, alerts, incident response, and monitoring for pipeline reliability. • Help retire brittle reporting paths and manage Snowflake governance, data contracts, and data observability.