Accenture Edge for AWS Tech Practice Leader – Data and AI

Accenture•St. Louis, MO

45d•$138,800 - $369,800•Hybrid

About The Position

Accenture Edge for AWS is seeking a Tech Practice Leader – Data and AI to build and lead the practice serving mid-market companies up to $3B in annual revenue. This leader owns the practice vision, offerings portfolio, architecture standards, delivery model, reusable assets, talent strategy, and governance required to modernize data foundations, deliver analytics and machine learning solutions, and scale secure, responsible generative AI and agentic AI applications on AWS. The ideal candidate combines deep data engineering, analytics, machine learning, and generative AI architecture credibility with field CTO-style customer engagement, presales solutioning, product and platform thinking, and delivery governance. This role is accountable for creating repeatable offerings across data strategy, lakehouse and data mesh architectures, data migration, governance, business intelligence, advanced analytics, machine learning, Amazon Bedrock, Amazon SageMaker AI, retrieval-augmented generation, intelligent agents, AI security, model evaluation, responsible AI, and AI operations.

Requirements

Minimum 15+ years of experience in data, analytics, AI, machine learning, cloud architecture, technology consulting, or transformation delivery, including 8+ years in hands-on data platform, analytics, ML, generative AI, or AWS architecture and delivery.
Minimum 5+ years leading and scaling a Data and AI practice, data platform organization, AI center of excellence, analytics service line, or cloud data engineering team, including teams of 25–50 technical professionals.
Proven accountability for $50M–$100M in annual Data and AI bookings and revenue, with demonstrated ability to create or influence $100M–$200M in qualified annual pipeline.
Minimum 5+ years experience solutioning, governing, or delivering at least 15 data, analytics, ML, or AI engagements, including at least 5 production AI or generative AI solutions and senior technical sponsorship for strategic accounts.
Minimum 5+ years expertise in AWS Data and AI architecture, including modern data platforms, governance, analytics, machine learning, Amazon SageMaker AI, Amazon Bedrock, retrieval-augmented generation, intelligent agents, security, and responsible AI.
Minimum 5+ years experience leading complex pursuits and executive workshops, translating prioritized use cases into data and AI roadmaps, value cases, production architectures, commercial models, and adoption plans.
Minimum 5+ years with executive-level communication and field CTO credibility with CIO, CTO, CDO, CAIO, CISO, business, data, engineering, risk, and legal leaders.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

Nice To Haves

Experience leading technical governance for 10+ large-scale data or AI programs, covering architecture quality, data governance, security, evaluation, responsible AI, delivery risk, and executive alignment.
Experience contributing to 10+ strategic pursuits, RFPs, executive workshops, proofs of value, or deal-shaping efforts annually.
Experience launching 3+ go-to-market sales plays or packaged offerings and enabling sellers with use-case qualification, discovery guides, value calculators, demos, pricing models, and customer proof points.
Knowledge of data products, lakehouse, data mesh, metadata, lineage, data quality, semantic layers, vector search, knowledge bases, model evaluation, guardrails, and AI observability.
Experience creating reusable data platform blueprints, governance frameworks, RAG patterns, agent templates, evaluation toolkits, or MLOps runbooks.
Experience building a newly formed practice, AI center of excellence, data product organization, service line, or incubation business, including talent development and AWS certification growth.
Familiarity with AWS partner programs, ACE, AWS Marketplace, funding mechanisms, competencies, joint technical validation, and packaged or outcome-based commercial models.
Published thought leadership, strong customer references, and relevant certifications such as AWS Certified Machine Learning Engineer – Associate, AWS Certified Data Engineer – Associate, AWS Certified Solutions Architect – Professional, or AWS Certified AI Practitioner.

Responsibilities

Build and lead the practice: Define the Data and AI strategy, offerings portfolio, investment priorities, operating model, delivery capacity, ecosystem partnerships, and growth roadmap.
Own portfolio and solution quality: Lead offerings across data strategy, data platforms, lakehouse and data mesh, data migration, governance, integration, business intelligence, advanced analytics, machine learning, generative AI, agentic AI, and AI operations.
Own bookings and revenue growth: Carry direct accountability for $50M–$100M in annual practice bookings and revenue, partner with sales leaders and AWS field teams to originate opportunities, qualify use cases, shape value propositions, and convert discovery sessions, data assessments, and proofs of value into production Data and AI pipeline.
Lead strategic pursuits: Act as the senior technical and commercial sponsor for priority deals, owning use-case prioritization, data readiness, architecture, value realization, responsible AI controls, estimation, executive presentations, proposal quality, and technical close.
Activate AWS co-sell and Marketplace: Create packaged Data and AI solutions that are easy for AWS sellers to position, support ACE progression, use applicable funding, and develop AWS Marketplace-ready offers with clear scope, price, outcomes, evaluation criteria, and procurement paths.
Build market demand: Create account-based campaigns, executive briefings, AI innovation days, workshops, assessments, and industry sales plays focused on data modernization, AI-ready foundations, generative AI, intelligent agents, responsible AI, and AI operations.
Establish thought leadership: Develop differentiated points of view on enterprise AI value, data readiness, productionizing generative AI, agentic operating models, responsible AI, model and data governance, adoption, and AI economics for mid-market customers.
Represent the practice externally: Publish articles and research-backed perspectives; speak at customer, analyst, industry, and AWS events; lead webinars and roundtables; and build relationships with CIO, CTO, CDO, CAIO, CISO, business, data, risk, and legal leaders.
Turn innovation into growth assets: Capture customer references, case studies, reusable demos, quantified benefits, evaluation results, adoption patterns, and lessons learned to improve credibility, cross-sell, repeatability, and win rates.
Serve as senior technical sponsor: Lead executive workshops, AI opportunity discovery, Data and AI roadmaps, architecture decisions, value cases, and technical governance for priority customers.
Industrialize AI delivery: Establish reusable patterns for retrieval-augmented generation, intelligent agents, model selection, evaluation, guardrails, prompt and knowledge management, observability, security, cost optimization, and lifecycle operations.
Strengthen data foundations: Ensure solutions address data quality, metadata, lineage, privacy, security, governance, interoperability, and structured and unstructured data readiness.
Ensure delivery readiness: Set clear scope, architecture, datasets, model choices, staffing, pricing assumptions, evaluation criteria, risks, controls, and business outcomes before handoff.
Develop technical talent: Build capability frameworks, certification paths, communities of practice, and mentoring for data architects, engineers, scientists, ML engineers, AI architects, and delivery leads.
Govern responsible AI: Establish architecture reviews, model and use-case governance, evaluation standards, human oversight, security-by-design, privacy controls, and escalation paths.
Drive practice performance: Own annual bookings and revenue attainment, and track practice-sourced and influenced pipeline, conversion, win rate, Marketplace activity, utilization, margin, time to value, solution reuse, model quality, adoption, customer references, thought-leadership reach, certification progress, customer satisfaction, and measurable business outcomes.