Engineering Manager (Data engineer)

The Nielsen Company; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

The Nielsen Company in Bengaluru seeks a Senior Engineering Manager to lead Data Engineering within the Digital & AI organization, driving high-scale data pipelines and measurement solutions across Streaming+ Infrastructure and PIE.

You will oversee end-to-end data lifecycle, mentor teams, own the tech roadmap, and collaborate with Product Management and Data Science to define system requirements for automated processing and eligibility.

Qualifications

• 12+ years of relevant experience.
• Deep expertise in Java and Spring Boot.
• Hands-on with Big Data technologies and ETL tools.
• Experience with AWS and monitoring tools.

Responsibilities

• Lead and mentor engineering teams, fostering technical excellence and automation.
• Own the Tech Roadmap and long-term planning for data engineering workstreams.
• Collaborate with Product Management and Data Science to define system requirements for automated data processing.
• Manage resource bandwidth and quarterly planning to ensure delivery.

Skills

Java

Spring Boot

Big Data

ETL

SQL

AWS

ClickHouse

Elasticsearch/OpenSearch

Spark

Logstash

Databricks

Tools

Databricks

Job description

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

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.

Qualifications

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.

Additional Information

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.

Hands‑on of Databricks.