Data Engineer, Engineering & Operations

We are seeking a Data Engineer to join the Engineering & Operations team. Reporting to the Director of Engineering & Operations, this role will design, build, and operate scalable data pipelines, clean room environments, identity workflows, and privacy-safe data integrations that support NBCUniversal’s data collaboration ecosystem. The ideal candidate is a hands-on data engineer with strong SQL and Python skills, experience working with cloud data platforms such as Snowflake or Databricks, and the ability to build secure, reliable, and repeatable data workflows for partner onboarding, audience activation, measurement, and reporting. Responsibilities Clean Room & Partner Onboarding Support partner onboarding into clean room environments across platforms such as Snowflake, LiveRamp, Databricks, or similar technologies. Follow clean room architecture patterns that are secure, scalable, privacy-preserving, and repeatable across partner engagements. Configure and manage clean room environments, including data access, environment setup, platform configuration, and release validation. Serve as the technical owner for assigned partner onboarding efforts, coordinating with product, engineering, operations, privacy, and partner-facing teams. Implement privacy-preserving controls such as aggregation thresholds, anonymization techniques, approved query patterns, and output validation checks. Infrastructure Setup & Library Deployment Deploy and manage Python-based libraries, templates, and reusable components within the clean room and data platform ecosystem. Support environment setup, configuration management, package deployment, and version-controlled release processes. Partner with software engineering teams to operationalize reusable libraries for audience, measurement, reporting, and partner-facing workflows. Ensure platform components are deployed consistently across partner environments and aligned with established engineering standards. Security, RBAC & Privacy Controls Design, implement, and enforce granular role-based access control policies across data platform environments. Configure least-privilege service accounts, roles, grants, schemas, shares, and data access patterns. Partner with security, privacy, and platform teams to ensure access controls meet internal policies and partner-specific requirements. Validate that partner-facing outputs adhere to privacy, security, and business requirements before release. Data Pipelines & Data Product Ownership Design, build, and operate scalable ELT pipelines using advanced SQL, Snowpark, PySpark, dbt, or similar technologies. Develop and provision curated Gold datasets for audience, measurement, activation, and reporting use cases. Build reusable pipeline patterns that support batch and near real-time processing across Snowflake, Databricks, or similar platforms. Translate business and analytical requirements into reliable, well-documented, production-ready data products. Own pipeline performance, reliability, data correctness, and operational support for assigned data products. Identity Resolution & Data Collaboration Implement and evolve identity resolution logic that maps internal NBCU data to third-party identifiers such as LUIDs, RampIDs, TransUnion IDs, or similar identity frameworks. Support privacy-safe identity workflows for audience matching, measurement, activation, and partner collaboration. Build validation checks to ensure identity mappings are accurate, secure, and compliant with approved usage patterns. Work with internal teams and external partners to troubleshoot match rates, data quality issues, and onboarding discrepancies. Data Quality, Testing & Validation Build automated data quality checks using tools such as Great Expectations, dbt tests, custom SQL assertions, or similar frameworks. Define and monitor quality standards for schema drift, null rate spikes, volume anomalies, duplicate records, referential integrity, and unexpected data distribution changes. Create test strategies for partner-facing releases, including input validation, output validation, regression testing, and privacy checks. Document data assumptions, known limitations, validation logic, and operational support procedures. FinOps & Operational Excellence Optimize query performance and platform costs through query tuning, clustering/partitioning strategies, caching, incremental processing, and workload management. Implement query tagging, workload tracking, and chargeback/showback models to improve cost transparency and partner-level attribution. Establish monitoring, alerting, runbooks, and standard operating procedures to improve platform reliability and reduce incident time-to-resolution. Participate in incident response, root cause analysis, and continuous improvement efforts for production data workflows.