Senior Data Partnerships Manager I

About the role

We are looking for a hands-on technical customer success leader to be the go-to expert for Nielsen s newest data products: Audience Builder, Data Driven Linear (DDL), data clean rooms, API-based offerings such as N1 advanced audiences, and the Media Data Engine (MDE).

Working from Bengaluru with clients and colleagues in the US, you will design how clients connect to our data, build the APIs, clean rooms and scripts that make it work, and help them get measurable value from it. You will also co-lead Quality 2.0, a program with Engineering that tracks data quality by its impact on customers, not just delivery metrics.

Responsibilities

Technical solutions and client integrations

• Lead technical discussions with client data, engineering and analytics teams, and design integrations that fit each clients systems.

• Set up and troubleshoot REST and GraphQL API integrations, including authentication, paging, rate limits and error handling.

• Configure and query data clean rooms (for example Snowflake, Databricks), and support audience matching and overlap analysis.

• Write production-quality SQL and Python scripts and reusable code samples that speed up client onboarding.

• Run in-depth technical training for internal teams and client technical staff.

Product expertise and adoption

• Become the expert on a portfolio of audience and measurement data products, including their data models, APIs and main client use cases.

• Support audience workflows that combine first-party, third-party and Nielsen data, and help clients activate audiences in downstream platforms through the UI, APIs and AI-agent connections.

• Help media sellers, agencies and ad tech platforms use measurement data through APIs for planning, pacing, forecasting and deal reconciliation.

• Lead onboarding and adoption for new API-based data products. Deliver tailored training, define best practices, track usage, and turn client feedback into product improvements.

Data quality and customer impact (co-lead with Engineering)

• Build and maintain a quality scorecard that combines delivery metrics, customer impact and customer feedback.

• Analyse escalation trends and customer feedback to find recurring issues and their root causes.

• Push for preventive controls, such as automated data-freshness monitoring, more reliable reprocessing, and contract tests before each release, to reduce quality issues reported by customers.

Customer success and advocacy

• Lead onboarding for new clients, with demos, training and learning materials tailored to their needs.

• Track customer health and adoption metrics, and run plans to grow usage and retention.

• Represent the client inside Nielsen, sharing their needs and market trends with Product, Engineering, Marketing and Sales.

• Resolve complex client issues quickly by working with internal teams across time zones. Qualifications

What you will bring

Must have

• 5+ years of experience, including at least 4 years in a client-facing technical role such as Technical Account Manager, Solutions Architect, Implementation or Data Consultant, or Technical Customer Success.

• Advanced SQL (CTEs, window functions, performance tuning) on a cloud data warehouse such as Snowflake, BigQuery, Redshift or Databricks.

• At least 2 years of hands-on Python for data work and automation (pandas, requests, scripting).

• Hands-on experience integrating REST APIs (Postman, OAuth/API keys, JSON, troubleshooting) and GraphQL APIs.

• Experience working directly with US or other global clients, including presenting to senior stakeholders.

• Excellent written and spoken English, and the ability to explain technical concepts clearly to non-technical audiences.

• Data clean rooms: Snowflake Data Clean Rooms, LiveRamp Habu, AWS Clean Rooms or Google Ads Data Hub.

• Knowledge of CI/CD pipelines.

• AWS & Data pipelines and quality tools: Airflow, dbt, redshift, monitoring and alerting.

Good to have

Media, ad tech or measurement: TV/CTV, GRPs/impressions, audience-based buying, programmatic (DSP/SSP), post-buy reconciliation.

AI and agent tools: LLM APIs, MCP or similar agent integrations.

BI tools (Tableau,powerbi) and work-tracking tools (Jira, ServiceNow, Salesforce).