• 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.

Role Description Fetch is looking for a Senior Data Engineer to join a cross-functional product team, working closely with machine learning engineers, backend engineers, and product managers to build robust data infrastructure that powers Fetch’s recommendation and audience targeting systems. In this role, you will take ownership of critical data infrastructure components, including: - Data transformation pipelines - Real-time event ingestion systems - Data contracts that ensure reliability across our platforms Your work will enable downstream services and applications to access high-quality, low-latency data at scale, processing terabytes of data daily. This position requires deep technical expertise in building scalable, reliable data systems, along with strong collaboration skills to translate cross-functional requirements into durable and well-designed solutions. Role Responsibilities - Design, build, and operate scalable data pipelines using batch and real-time processing technologies such as Apache Spark, Kafka, Flink, or managed cloud streaming services to process terabytes of data daily. - Build data infrastructure that ingests real-time events and stores them efficiently across databases, data warehouses, and data lakes within AWS. - Establish and enforce data contracts with backend engineering teams by implementing schema management, data quality checks, and monitoring to ensure pipeline reliability. - Make data accessible and consumable for operational services, analytics platforms, and data-intensive product features, balancing latency, freshness, and accuracy requirements. - Collaborate closely with backend engineers, machine learning engineers, and product partners to understand data access patterns, system constraints, and quality expectations. - Take ownership of significant portions of the data platform architecture, driving design decisions and technical prioritization. - Develop tools, frameworks, and recommended patterns that enable rapid development of data products and consistent pipeline deployments. - Mentor engineers on data engineering best practices and raise the overall quality bar across the organization. - Stay current with emerging technologies in data processing and infrastructure, evaluating their applicability and impact on Fetch systems. Recommendation Systems Team In this role, you will focus on building data infrastructure that powers Fetch’s recommendation systems. You will partner closely with machine learning engineers to design and implement the platforms and pipelines that enable personalized recommendations at scale. Key areas of focus include: - Building and maintaining feature store infrastructure to support efficient feature development, discovery, and reuse across recommendation models. - Designing and operating low-latency feature serving systems that power real-time recommendation APIs for both training and inference workloads. - Implementing monitoring and quality checks to ensure feature freshness, accuracy, and consistency. - Collaborating with ML engineers to understand feature access patterns, model requirements, and latency and throughput needs. Qualifications - 6+ years of professional experience in data engineering, building and operating production data systems at scale. - Proven experience designing, building, and maintaining scalable batch and real-time data pipelines capable of processing terabytes of data daily. - Hands-on experience with modern data processing frameworks such as Apache Spark, Kafka, Flink, Open Table Formats, and modern OLAP databases. - Strong foundation in data architecture principles, including data modeling, schema design, and tradeoffs between latency, reliability, and cost. - Proficiency in at least one modern programming language such as Go, Python, Java, or Rust, along with strong SQL skills. - Experience with Infrastructure as Code tools such as Terraform or CloudFormation in a production environment. - Familiarity with CI/CD processes and modern software development lifecycle practices, with an emphasis on shipping incrementally and improving systems over time. - Experience implementing data quality controls, including validation, monitoring, and anomaly detection. - Ability to take ownership of projects with guidance, driving designs from initial architecture through implementation and adoption. - Comfort presenting technical designs, participating in peer reviews, and constructively challenging decisions. - Strong collaboration skills with experience working closely with software engineers, machine learning engineers, data analysts, and product partners. - Undergraduate or graduate degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience. Compensation At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. The base salary range for this position is $149,523 - $206,578. Discover our benefits and how our employees live rewarded at https://fetch.com/careers .

• Build and maintain data pipelines that power analytics, ML workloads, and product-facing applications • Evolve our data platform alongside cloud platform engineers • Design and implement scalable data infrastructure • Develop data services and APIs that expose trusted data to product applications • Own data quality and observability • Partner with AI engineers, data scientists, analysts, and product teams to understand their data needs • Uphold strong data privacy, security, and compliance practices