Forward-Deployed Delivery Lead (AI & Data)

Confiz•Atlanta, GA

4d•Hybrid

About The Position

Confiz is seeking a Forward-Deployed Delivery Lead focused on AI and data, combining technical expertise, strategic delivery leadership, and a customer-first mindset. The role embeds directly within customer environments to understand business objectives and technical architecture, lead distributed delivery teams, and ensure successful execution of AI- and data-driven solutions. The position is based in Atlanta, Georgia, with a hybrid arrangement requiring at least three days onsite per week. The Delivery Lead serves as the primary onsite customer contact and owns end-to-end delivery and solution architecture for strategic engagements. This includes connecting business objectives to technical solutions, preparing for customer meetings with concrete deliverables, explaining data and architecture, and guiding delivery teams. The role also manages scope, plans, milestones, risks, stakeholder expectations, and delivery ceremonies; communicates progress to leadership; and translates customer needs into actionable direction for distributed and offshore teams. The ideal candidate has extensive experience delivering AI, data, or analytics engagements, including customer-facing delivery leadership. They bring hands-on knowledge of Databricks or comparable lakehouse platforms, SQL, data and AI architecture, and LLM-based agent systems. Confiz describes a global team working on enterprise projects and products for customers including Fortune 100 retail and CPG companies, store chains, fintech companies, and startups. The company highlights its focus on processes and culture, certified management systems, collaborative learning, and a fun workplace.

Requirements

10–12+ years of professional experience delivering data, analytics, or AI engagements, including at least 3 years in customer-facing delivery leadership.
Hands-on experience with Databricks or equivalent lakehouse platforms, including Unity Catalog, Delta Tables, Workflows, and data governance.
Strong SQL skills and practical experience with notebooks, including the ability to quickly understand and explain unfamiliar data schemas.
Working knowledge of LLM-based agent architectures, including Retrieval-Augmented Generation (RAG), tool usage, orchestration, and evaluation.
Strong understanding of end-to-end data and AI solution architecture, including ingestion, data modeling, governance, and agentic platforms.
Ability to quickly understand new business domains and establish credibility with business and technical stakeholders.
Experience managing distributed, offshore, or cross-functional engineering teams.
Demonstrated ability to manage project delivery, stakeholder expectations, risks, scope, and competing priorities.
Excellent verbal and written communication skills, including the ability to engage with executive leadership and technical teams.
Strong analytical, problem-solving, and decision-making skills, and the ability to work independently in ambiguous environments.
Willingness and ability to work onsite with customers in Atlanta, Georgia, at least 3 days per week.
Proficiency in using AI tools to enhance daily productivity and delivery workflows.
Bachelor's degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related field.
At least 3 years of experience leading customer-facing technology delivery engagements.

Nice To Haves

Databricks Data Engineer or Generative AI Engineer certification.
Hands-on experience with Databricks Genie, Mosaic AI, and agent development tools.
Previous experience as a Solution Architect, Data Engineer, or Product Manager before transitioning into delivery leadership.
Consulting or professional services background, with accountability for project delivery and account health.
Experience with Retail, Consumer Packaged Goods (CPG), or Financial Technology (FinTech) organizations, preferably in data-intensive environments with multiple business units.
Experience establishing and implementing enterprise data governance frameworks across multiple business units.
Active use of AI tools to accelerate research, technical problem-solving, and delivery workflows.
Ability to connect technical implementations with measurable business outcomes and customer priorities.
Comfort working directly within customer environments, independently navigating technical ambiguity, and owning outcomes from discovery through delivery.

Responsibilities

Serve as the primary onsite customer contact, engaging with executive sponsors, business stakeholders, architects, and engineering teams.
Develop and maintain an understanding of the customer's business model, business units, KPIs, priorities, and stakeholders.
Own end-to-end delivery of strategic AI and data engagements, aligning business objectives, technical solutions, and delivery outcomes.
Prepare for customer meetings through research and provide deliverables such as technical findings, prototypes, query results, and recommendations.
Own end-to-end solution architecture, including data ingestion, data modeling, governance, AI agents, and underlying platforms.
Interpret and explain data schemas and independently write and execute SQL queries to address customer questions and requirements.
Understand and explain Databricks agents and agentic platforms, including their architecture, capabilities, limitations, and implementation.
Present technical designs, solution demonstrations, and architectural trade-offs to executive, architectural, and technical stakeholders.
Review delivered solutions for alignment with customer business needs and expected outcomes.
Manage engagement scope, delivery plans, milestones, dependencies, risks, and decision logs.
Lead delivery ceremonies, including sprint planning, daily standups, project reviews, and executive steering meetings.
Manage scope changes, competing priorities, and stakeholder expectations while maintaining delivery quality and timelines.
Provide clear project updates to Confiz and customer leadership.
Translate customer requirements, business context, and feedback into actionable direction for distributed and offshore engineering teams.
Ensure engineering teams understand the business purpose of their work and can communicate project progress and technical solutions.
Mentor team members and encourage engineers to present their work in customer meetings and demonstrations.
Identify emerging risks, changing business priorities, and account growth opportunities, and provide proactive recommendations.
Use AI tools in daily workflows to improve research, preparation, technical problem-solving, and delivery efficiency.