Senior Engineering Manager - Data Engineering

Company Description

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.

Job Description

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.