We are seeking a Data Processing Manager to lead and scale UAT data processing and validation capabilities across complex data platforms. This role is accountable for UAT execution strategy, data readiness, automation, test‑bed governance, and release confidence, ensuring data quality before production deployments. 

The Data Processing Manager combines technical depth, execution ownership, and people leadership. You will manage a team of data processing engineers, drive process maturity, expand automation and AI adoption, and partner closely with Data Engineering, QA, Reporting, and Business teams. 

Key Responsibilities 

UAT Strategy, Governance & Delivery Ownership 

• Own the end‑to‑end UAT data processing strategy across releases and PIs 
• Define UAT governance including:  
• Entry / exit criteria 
• Execution checkpoints 
• Sign‑off standards 
• Ensure UAT execution is:  
• Predictable 
• Repeatable 
• Business‑aligned 
• Proactively manage risks, dependencies, and escalations related to UAT data readiness 

Team Leadership & Capability Building 

• Lead, coach, and develop Mid and Senior Data Processing Engineers 
• Define clear ownership for:  
• UAT planning 
• Job execution 
• Validation 
• Automation 
• Data maintenance 
• Build a culture of:  
• Accountability and ownership 
• Continuous improvement 
• Automation‑first thinking 

Data Processing & UAT Execution Oversight 

• Ensure teams fully understand and validate changes delivered by development teams across ELT datapipelines  
• Oversee execution of:  
• Full refreshes 
• Incremental loads 
• Backfills and reprocessing 
• Ensure execution failures are:  
• Diagnosed quickly 
• Properly documented 
• Resolved or escalated appropriately 

UAT Data Management & Test Bed Governance 

• Own UAT data maintenance strategy, including:  
• Refresh cadences 
• Data masking/anonymization 
• Stability across releases 
• Govern the design and upkeep of UAT test beds, ensuring:  
• Production‑like datasets 
• Support for functional, regression, and edge‑case testing 
• Reusability across releases 
• Ensure UAT datasets remain relevant, reliable, and well‑documented 

Automation & Quality Enablement 

• Drive automation strategy for UAT data validation:  
• Functional checks 
• Regression validations 
• Reconciliation frameworks 
• Ensure automation coverage improves release over release 
• Reduce manual effort and UAT cycle times through repeatable tooling 
• Promote shift‑left validation and early defect detection 

Defect Management & Continuous Improvement 

• Oversee defect lifecycle management:  
• Failure logging 
• Root cause analysis 
• Bug prioritization 
• Re‑validation and closure 
• Analyze defect trends across releases 
• Partner with engineering teams to address recurring and systemic issues 
• Define and track UAT quality KPIs (escape rate, repeat failures, coverage) 

End‑to‑End Validation & Business Confidence 

• Ensure end‑to‑end data validation from:  
• Source systems 
• Data warehouse 
• Reports and dashboards 
• Validate business rules, aggregations, treatments, and calculations 
• Ensure business and reporting stakeholders receive clear, reliable data sign‑off 
• Act as the final UAT data quality gate before production release 

AI Adoption & Productivity Enablement 

• Lead responsible AI adoption across the data processing function 
• Promote the use of AI tools (e.g., GitHub Copilot, SQL & log analysis assistants) to:  
• Speed root cause analysis 
• Improve validation coverage 
• Accelerate automation development 
• Enhance documentation and playbooks 
• Define guardrails to ensure AI‑generated outputs are validated and trusted 
• Track AI adoption and productivity improvements 

Stakeholder Communication & Data Stand‑Ups 

• Lead or oversee data stand‑up activities, providing:  
• Execution status 
• Risk visibility 
• Defect summaries 
• Act as the primary point of contact for:  
• Data Engineering 
• QA 
• Reporting / BI 
• Product and Business teams 
• Communicate UAT readiness, risks, and recommendations clearly to leadership 
• Responsible AI usage 
• Support career growth, performance management, and skill development 

• 10–12 years of experience in data processing, data validation, analytics operations, or data engineering 

• 3+ years in a people‑management or team‑lead role 

• Strong understanding of:  
• Dimensional and fact‑based data models 
• Batch and incremental data processing 
• UAT execution and release cycles 
• Expert‑level SQL knowledge for validation and analysis 
• Proven experience building or scaling:  
• UAT processes 
• Automation frameworks 
• Test beds and data maintenance practices 
• Strong leadership, communication, and stakeholder‑management skills 
• Experience driving AI‑enabled productivity within engineering or operations teams 

Nice‑to‑Have Skills 

• Experience with Snowflake or cloud data platforms 
• Python knowledge for validation and automation use cases 
• Familiarity with orchestration tools (Airflow, ADF, Control‑M) 
• Exposure to data quality frameworks and monitoring tools 
• Experience partnering with QA, Product, and Business leadership 
• Certifications or training in Agile, Data, or AI domains 

Our Benefits

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)

NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://www.nielseniq.com/global/en/ai-safety-policies.

About NIQ

NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state-of-the-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.

For more information, visit NIQ.com

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Our commitment to Diversity, Equity, and Inclusion

At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence.  All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion