Data Validation Risk - Manager

PricewaterhouseCoopers•Atlanta, NY

About The Position

As a Data Validation Risk Manager, you will lead client support work focused on data engineering, data validation, and risk-aware delivery across data pipelines, integration points, and transformation activities within PwC’s Advisory practice. You will coach team members, direct workstreams, manage performance, and review deliverables with sound judgment while addressing issues and keeping work aligned with client needs and Firm values. You will help convert raw data into usable information, apply technology and innovation to improve delivery, and take ownership of planning, execution, and completion across assigned projects. The position may require travel up to 60%.

Requirements

At least a Bachelor's degree.
At least 5 years of experience.
Experience demonstrating data validation, quality control, and risk analysis.
Experience managing client-facing workstreams within data engineering or analytics environments.
Experience leading teams through planning, budgeting, execution, and delivery.
Knowledge of data integration, modeling, and pipelines applied to business issues.
Experience mentoring junior team members through feedback and coaching sessions.
Ability to communicate findings clearly to technical and business audiences.

Nice To Haves

Preference for study in one or more of these fields: Analytics/Data Science, Business Administration/Management, Computer Science/Information Systems, Economics, Engineering, Finance, Mathematics/Statistics, or Operations/Supply Chain.

Responsibilities

Lead data validation and risk workstreams across client engagements, translating business needs into practical data engineering solutions.
Manage project plans, timelines, budgets, and deliverables while directing team efforts across multiple workstreams.
Develop and review data pipelines, integration flows, transformation logic, and validation controls to support reliable data processing.
Apply knowledge of data architecture, modeling, quality, and security to identify issues and strengthen data solutions.
Analyze complex data structures and system interactions to trace risks, gaps, and downstream impacts.
Coach team members through feedback, review, and day-to-day guidance to support development and performance.
Collaborate with client leaders and internal stakeholders to align priorities, clarify requirements, and address delivery challenges.
Leverage data engineering platforms, database optimization, and query tuning techniques to improve solution performance.
Promote innovation through automation, AI-enabled tools, and scalable engineering methods.
Uphold technical standards, documentation discipline, and independence requirements throughout delivery.