Director, Data Engineering & AI

West Monroe•Washington, DC

1d•$200,000 - $250,000•Hybrid

About The Position

West Monroe has an opportunity for a strategic Director, Data Engineering & AI to join our Technology practice. This leader will structure, lead, support, drive, and grow West Monroe’s Technology Data Engineering practice and associated offerings in collaboration with other leaders across the company.

Requirements

10+ years participating in team-based, client-facing business and technology consulting services
Requisite depth in one or more key data platform technologies – Databricks, GCP, and / or Snowflake
Entrepreneurial spirit and willingness to innovate on new and emerging solutions
Experience designing and delivering modern data platforms that support AI, machine learning, and Generative AI use cases at enterprise scale
Strong understanding of AI-oriented data architecture concepts including real-time data pipelines, feature engineering, vector databases, semantic search, and unstructured data processing
Experience implementing scalable data engineering solutions within Databricks, Snowflake, AWS, Azure, and/or GCP environments to support AI and analytics workloads
Familiarity with MLOps, LLMOps, data observability, and governance frameworks required for enterprise AI deployment and operationalization
Demonstrated success within one or more of West Monroe’s core industries including Healthcare (payer/provider, life sciences), Utilities, Insurance, Banking, and/or Private Equity
A commitment to inclusion and diversity, and openness to new ideas and perspectives
Ability to Travel, as required based on client and practice demands (30 to 50%)
Hybrid role, expectation to go into your local West Monroe office 1 to 2 days a week

Responsibilities

Drive opportunity origination at existing clients, qualify leads and collaborate on pursuits for new work and build deep relationships
Shape client engagements focused on AI-enabled data modernization, including data platform transformation, data quality, governance, and AI infrastructure readiness
Collaborate with industry and multidisciplinary teams to identify sales opportunities
Initiate and lead business development meetings with prospective clients
Understand client needs and requirements and help turn those goals into concrete engagements and detailed proposals that highlight quantifiable value creation
Create work plans, pricing estimates, and risk assessments for prospects
Attend networking events and actively build and leverage a professional network and affiliate network in the local community
Lead teams serving clients across industries while demonstrating executive presence with high level client counterparts.
Manage and influence multi-disciplinary teams to translate client business objectives into data and analytics solutions; collaborate with data engineering, industry, and product teams to deliver impactful results for our clients
Provide quality assurance and conduct frequent touch bases with client executives to ensure value is realized and understood from our engagements
Architect enterprise data solutions that support AI use cases through high-quality, governed, discoverable, and interoperable data pipelines
Guide clients in implementing data engineering best practices for AI applications, including feature engineering, vectorized data architectures, metadata management, and real-time data processing
Understand and support the underlying business processes and technologies being used within an organization to be able to make recommendations on how to achieve long-term scalability, reduce operational cost, or better support business processes
Ensure proper management of project economics including: project budgets, invoicing, and the collection process
Collaborate with national and local leadership within Data Engineering & Analytics practice and across West Monroe industry and functional practices, to develop and deliver multi-disciplinary, data-driven offerings
Develop and expand West Monroe’s capabilities around AI-enabled data engineering, intelligent data platforms, and cloud-native AI architectures
Build accelerators, frameworks, and reusable assets that support AI data readiness, unstructured data processing, semantic layers, and vector database integration
Drive internal innovation around emerging AI engineering patterns including retrieval augmented generation (RAG), agentic workflows, streaming data architectures, and AI orchestration frameworks
Mentor teams on modern AI data engineering concepts including scalable pipeline design, data products, feature stores, and AI platform operationalization
Stay current on evolving technologies across AI infrastructure, data engineering, cloud ecosystems, and intelligent data applications