Senior Manager, Solutions Architecture - Data & AI

Responsibilities

• Presale: Lead scoping, technical design, and work planning during the presale process, articulating our data and AI capabilities to prospective clients.
• Delivery: Play the lead role in technical architecture and discovery projects, using consulting methods to refine the data product and technical architecture of client projects. This includes designing scalable data pipelines, machine learning model architectures, and MLOps frameworks.
• Subject Matter Expertise: Own a "technology vertical" within Data & AI, encompassing solution design, platform selection (e.g., Databricks, Snowflake, AWS Bedrock), implementation support, and thought leadership. This may include areas such as large-scale data warehousing, real-time analytics, machine learning operations (MLOps), or agentic AI systems.
• Roving Catalyst: Enhance existing project teams with architectural account-level guidance or specific subject matter expertise in data engineering, machine learning, or artificial intelligence.

Qualifications

• Multi-platform familiarity , with expertise in one or more of the following: Cloud Data Platforms: Deep experience with Databricks , Snowflake , or other major cloud data warehousing/lakehouse solutions. Cloud AI/ML Services: Proficiency with cloud-native AI/ML platforms (e.g., AWS Bedrock / Sagemaker, Azure ML, Google AI Platform/Vertex AI). Backend Cloud Platforms: Strong experience with major cloud providers (AWS, Azure, GCP) for deploying and managing data and AI solutions.
• Cloud Data Platforms: Deep experience with Databricks , Snowflake , or other major cloud data warehousing/lakehouse solutions.
• Cloud AI/ML Services: Proficiency with cloud-native AI/ML platforms (e.g., AWS Bedrock / Sagemaker, Azure ML, Google AI Platform/Vertex AI).
• Backend Cloud Platforms: Strong experience with major cloud providers (AWS, Azure, GCP) for deploying and managing data and AI solutions.
• Data & AI Industry Expertise: Specialized knowledge of the data ecosystem, including data ingestion, ETL/ELT processes, data warehousing, data lakes, and real-time analytics.
• Machine Learning & AI Acumen: Candidates should be aware of current trends in machine learning (supervised, unsupervised, deep learning), MLOps, and advanced AI concepts like Agentic AI or Large Language Models (LLMs).
• Technical Proficiency: Deep understanding of MLOps principles and tools for continuous integration/continuous delivery (CI/CD) of machine learning models. Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable deployments.
• Deep understanding of MLOps principles and tools for continuous integration/continuous delivery (CI/CD) of machine learning models.
• Familiarity with containerization (Docker) and orchestration (Kubernetes) for scalable deployments.
• Experience with software estimation , work planning, and building project plans within time/cost constraints.
• An eye for details and an ability to navigate ambiguity.
• Experience delivering software or data solutions in an agency/client-service environment.
• Ability to clearly break down complex data and AI concepts to both technical and non-technical individuals, including executive stakeholders.
• Experience in having commercial conversations involving budgets and fees related to data and AI initiatives.
• Passion to apply modern processes that transform client organizations (e.g., continuous delivery, rapid iteration, and agile-inspired methodologies for data and ML).
• Excellent verbal and written communication skills.
• Ability to use repeatable frameworks and best practices to incorporate new data technologies or AI concepts.
• Appreciation for the wider client enterprise, beyond a single data project or AI initiative, and a desire to expand an account to service a client holistically.
• Equally comfortable working in pre-sale and post-sale environments.

Equal Opportunity Employer