Nielsen 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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