Technical Delivery Lead – Data & AI

DescriptionThe Technical Delivery Lead – Data & AI is a senior, client‑facing leader responsible for end‑to‑end delivery of complex data engineering, analytics, and AI/ML engagements. This role blends hands‑on technical architecture, delivery leadership, and executive‑level client engagement, serving as a critical anchor for large‑scale, long‑cycle consulting programs.The ideal candidate brings deep expertise in modern cloud data platforms, proven experience delivering multi‑phase enterprise programs, and the executive presence to partner with C‑suite stakeholders across industries including Financial Services, Life Sciences, Consumer Products, and beyond.Key ResponsibilitiesDelivery LeadershipOwn full lifecycle delivery accountability for complex, multi‑workstream Data & AI engagements, from kickoff through hypercare and steady‑state handoff.Establish and manage delivery governance, including project cadence, sprint reviews, RAID management, executive steering committees, and formal change control (e.g., CAB processes).Lead and coordinate cross‑functional delivery teams (5–15+ resources), including data engineers, ML engineers, BI developers, and nearshore/offshore contributors.Translate business objectives into scalable technical architectures and phased delivery roadmaps; manage scope, timeline, and resourcing trade‑offs with clients and internal stakeholders.Ensure delivery excellence and production readiness across all outputs, including data pipelines, analytics layers, AI/ML models, dashboards, and governance artifacts.Technical Architecture & SolutioningLead solution design for enterprise data platform initiatives, including cloud migrations, lakehouse architectures, data mesh, Unity Catalog implementations, and modernization programs.Architect and oversee ETL/ELT pipelines, data quality frameworks, and semantic layers leveraging technologies such as Databricks, dbt, Apache Spark, Airflow, and major cloud platforms (AWS, Azure, GCP).Guide AI/ML solution delivery, including feature engineering, model development, MLOps pipelines, model monitoring, and production deployment.Evaluate and recommend tools across the modern data and AI ecosystem, including orchestration, observability, data quality, vector databases, and AI governance solutions.Act as the senior escalation point for complex technical challenges across active engagements.Client Engagement & Business DevelopmentServe as the senior delivery‑phase relationship owner, operating as a trusted advisor to executive and technical stakeholders.Identify expansion opportunities and contribute to account growth during ongoing engagements.Support pre‑sales activities, including discovery workshops, proposal development, RFP responses, SOW authoring, architectural solutioning, and pricing inputs.Participate in executive briefings, QBRs, and roadmap discussions, translating complex technical concepts into clear business narratives.Collaborate with practice and sales leadership to develop packaged offerings, accelerators, and repeatable delivery frameworks.People & Practice DevelopmentMentor and develop consultants at multiple levels, providing technical guidance, career coaching, and performance feedback.Contribute to internal intellectual property, including delivery playbooks, reference architectures, estimation models, and case studies.Represent the practice within partner ecosystems and at industry events, client forums, and thought‑leadership engagements.Required QualificationsExperience8+ years of experience in data engineering, analytics, or AI/ML roles.Minimum 3+ years in a consulting or professional services environment leading client‑facing engagements.Proven success delivering complex, multi‑phase data or AI programs valued between $500K and $5M+ on time and within budget.Experience managing distributed, cross‑functional teams in matrixed delivery models, including offshore and nearshore resources.Technical ExpertiseProduction‑scale experience with modern cloud data platforms such as Databricks (Delta Lake, Unity Catalog, MLflow), Snowflake, BigQuery, or Azure Synapse.Strong background in data engineering and orchestration tools, including dbt, Apache Spark, Airflow, Kafka, or comparable technologies.Experience operationalizing ML models, with familiarity in LLMs, RAG architectures, and AI governance principles.Hands‑on knowledge of data governance, cataloging, lineage, access control, and quality frameworks (e.g., Unity Catalog, Collibra, Alation).Proficiency with at least one major cloud provider (AWS, Azure, or GCP), including working knowledge of DevOps and Infrastructure‑as‑Code practices (Terraform, CI/CD).Leadership & Professional SkillsStrong executive presence with the ability to present to C‑level audiences and facilitate senior stakeholder discussions.Excellent written and verbal communication skills, capable of producing client‑ready architecture documents, executive presentations, and SOWs.Structured, analytical problem‑solver comf