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Job DescriptionNielsen is seeking a technical and strategic Senior Engineering Manager to lead our Data Engineering teams within the Digital & AI organization. This role is critical for driving the development of high-scale data pipelines and measurement solutions, including our Streaming+ Infrastructure and the Panelist Intelligence Engine (PIE).
The ideal candidate will oversee the end-to-end data lifecycle—from ingestion and processing to validation and reporting—ensuring the highest standards of data accuracy and platform integrity for our global digital measurement products.
Key Responsibilities
Leadership & Strategy
Lead and mentor high-performing engineering teams, fostering a culture of technical excellence, automation, and continuous improvement.
Own the Tech Roadmap and long-term planning for data engineering workstreams, ensuring alignment with product goals and business efficiency initiatives.
Partner closely with Product Management (Head of Streaming+ Infrastructure) and Data Science to define system requirements for automated panel data processing and eligibility.
Manage resource bandwidth and prioritize quarterly planning integrity over ad-hoc requests to ensure project delivery.
Technical Execution
Oversee the architecture and scaling of data pipelines using technologies like Spring Boot, ClickHouse, OpenSearch, and Logstash.
Drive the transition toward automated, longitudinal data processing to replace deterministic manual processes.
Ensure robust data validation across complex event streams, batch jobs, and downstream reporting dashboards.
Implement rigorous performance standards and "tech excellence" line items, such as Cloud Cost Optimization and SEV-1 Reduction targets.
Resolve critical technical blockers related to data schemas, device-process date misalignment, and international market integrations.
Implement and oversee ETL job monitoring and performance optimization for critical data pipelines.
Technical Proficiency
Backend & Frameworks: Deep expertise in Java and Spring Boot.
12+years of relevant experience.
Data Systems: Hands-on experience with Big Data technologies (ClickHouse, ElasticSearch/OpenSearch, Spark, MDL) and ETL tools (Logstash).
Cloud Ecosystems: Advanced knowledge of AWS services, including Lambda, S3, and EC2, along with Grafana for monitoring and observability.
Data Validation: Strong SQL skills and experience validating large-volume, high-variability datasets and event pipelines.
Data Pipeline Management: Strong experience with ETL job monitoring, optimization, and building/scaling high-volume data pipelines.
Leadership Capabilities
Proven experience managing engineering teams in an Agile environment with a focus on roadmap visibility and stakeholder communication.
Ability to conduct rigorous performance calibration and talent assessment to maintain a high-quality engineering bar.
Strong experience in cross-functional collaboration across Product, Data Science, and Operations teams.
Nice-to-Have Skills
Experience in digital measurement, panel management, or attribution ecosystems.
Knowledge of Connected TV (CTV) platforms and streaming data collection.
Familiarity with privacy-compliant, software-first measurement solutions.
Handson of Databricks.
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