AI & Data Lead - Director

PricewaterhouseCoopers•Washington DC, DC

About The Position

As an AI & Data Lead - Director at PwC, you will shape data modernization efforts within the Advisory practice, guiding client conversations and delivery across data engineering, cloud platforms, and AI-enabled analytics. You will set strategic direction, lead business development, oversee multiple initiatives, engage with clients at the executive level, and mentor teams to support their growth and delivery of sustained value. The role guides work across data platform modernization, ETL, cloud migration, and hybrid environments. You will help clients transition legacy data environments toward cloud-native architectures across AWS, Azure, Google Cloud Platform, Databricks, and Snowflake.

Requirements

At least a Bachelor's degree.
At least 9 years of experience.

Nice To Haves

Preference for study in one or more of the following fields: Analytics/Data Science, Business Administration/Management, Computer Science/Information Systems, Economics, Engineering, Finance, Mathematics/Statistics, or Operations/Supply Chain.
Demonstrated AI fluency across data engineering and modernization initiatives.
Experience leading cloud data platform strategy using AWS, Azure, Databricks, or Snowflake.
Ability to translate data architecture vision into scalable client solutions.
Experience driving data integration, quality, modeling, and pipeline modernization.
Experience guiding cross-functional teams through strategic delivery and change.
Experience shaping go-to-market priorities for data modernization offerings.

Responsibilities

Lead data engineering strategy for cloud modernization initiatives across client engagements.
Design data architectures, pipelines, and integration patterns for AWS, Azure, Databricks, and Snowflake environments.
Oversee data platform modernization delivery, including migration, consolidation, and performance tuning.
Develop scalable data models, warehouse structures, and transformation logic that support analytics and AI use cases.
Apply database management, indexing, query optimization, and security methods to improve platform reliability.
Guide teams in building and maintaining data lake and data warehouse solutions that support multiple business needs.
Translate client requirements into engineering roadmaps, delivery plans, and technical solution designs.
Drive adoption of data engineering platforms, automation tools, and agile delivery methods across projects.
Advise on data quality, governance, and infrastructure choices that support long-term platform value.
Influence technical decisions and shape solutions that connect business goals with modern data capabilities.
Lead business development, oversee multiple initiatives, engage executive-level clients, and mentor teams.