[Hiring] Databricks Center of Excellence Technical Leader @Perficient

Role Description

We currently have a career opportunity for a Databricks Center of Excellence Technical Leader to join our team located in the United States. The Databricks Center of Excellence Technical Leader is the senior technical authority for the Databricks practice. This role defines the technical vision, architecture standards, engineering methods, and quality bar for Databricks solutions while helping shape strategic client opportunities and grow a high-performing technical community.

The role is intentionally focused on Databricks technical leadership. It does not own every data, integration, analytics, or AI discipline. Instead, it partners across those disciplines to ensure Databricks solutions are secure, scalable, cost-effective, differentiated, and aligned to client outcomes. Additionally, you will foster an engaging colleague culture that inspires strong team and individual performance, in line with Perficient's People Promise.

What This Role Owns

• Technical direction for the Databricks practice, including platform architecture, engineering standards, reusable patterns, and solution quality.
• Architecture and technical governance for strategic pursuits and complex client engagements.
• Databricks-specific skills development, mentoring, certification strategy, and technical career growth.
• Technical alignment with Databricks field solution architects, product teams, and partner engineering stakeholders.
• The evolution of AI-first and agent-assisted delivery practices within the Databricks engineering community.
• External technical credibility through client workshops, technical content, community participation, and industry events.

Responsibilities

• Technical Strategy and CoE Leadership
  • Define and execute the technical strategy and roadmap for the Databricks Center of Excellence.
  • Establish architecture principles, delivery patterns, engineering standards, and technical governance for Databricks engagements.
  • Evaluate emerging Databricks capabilities and adjacent technologies, recommending adoption when they create measurable client or delivery value.
  • Create mechanisms to reuse proven architectures, accelerators, code assets, and lessons learned across engagements.
  • Partner with practice, alliance, industry, sales, and delivery leaders while retaining clear accountability for Databricks technical excellence.
• Solution Architecture and Technical Governance
  • Lead architecture for complex enterprise Databricks solutions across data engineering, analytics, governance, AI/ML, and real-time use cases.
  • Guide solution design across Unity Catalog, Delta Lake, Lakeflow, MLflow, Mosaic AI, Vector Search, Genie, Feature Store, Lakebase, and related platform capabilities.
  • Lead architecture reviews, design sessions, technical risk assessments, and major system decisions.
  • Define secure, scalable, high-performance, and cost-effective patterns across Azure, AWS, and Google Cloud.
  • Champion DataOps, MLOps, CI/CD, infrastructure as code, automated testing, observability, data quality, privacy, and security by design.
  • Remain sufficiently hands-on to lead technical spikes, proofs of concept, reference implementations, and resolution of critical architecture challenges.
• Client and Pre-Sales Technical Leadership
  • Serve as a trusted technical advisor to client executives, architects, and engineering leaders.
  • Translate business objectives into Databricks platform strategies, modernization roadmaps, and implementable architectures.
  • Lead technical discovery, feasibility assessment, solution shaping, estimation, architecture presentations, workshops, and proposal development.
  • Partner with sales and account teams to strengthen technical credibility, reduce solution risk, and improve pursuit outcomes.
  • Provide technical oversight for strategic engagements without becoming the day-to-day delivery manager for every project.
• AI-First and Agentic Engineering
  • Define practical standards for AI-assisted architecture, engineering, testing, code review, documentation, and delivery within the Databricks practice.
  • Guide solution patterns for Mosaic AI, agent orchestration, retrieval-augmented generation, model evaluation, LLMOps, MLOps, and tool-use workflows.
  • Build and govern reusable agentic delivery patterns that improve team productivity while preserving engineering quality, security, and human accountability.
  • Help technical teams apply AI tools responsibly to compress solution design and delivery cycles.
• Technical Talent and Community Development
  • Build and mentor a strong community of Databricks architects, engineers, and AI practitioners.
  • Define Databricks competencies, technical career expectations, learning paths, certification priorities, and mentoring practices.
  • Coach through architecture reviews, paired problem solving, design critique, and hands-on technical leadership.
  • Identify capability gaps and partner with practice leadership on hiring, staffing, succession, and development priorities.
  • Foster a culture of collaboration, accountability, continuous learning, inclusive leadership, and engineering excellence.
• Databricks Partnership and Thought Leadership
  • Serve as a primary technical counterpart to Databricks field solution architects, product teams, and partner engineering leaders.
  • Provide partner-side product feedback and bring emerging platform knowledge back into the practice.
  • Collaborate on joint solutions, client opportunities, technical enablement, and go-to-market priorities.
  • Publish technical guidance, reference architectures, blogs, whitepapers, demos, and reusable solution content.
  • Represent the practice at Databricks and industry events through presentations, panels, workshops, and community engagement.

Expected Outcomes

• Consistent, high-quality Databricks architecture and engineering practices across client engagements.
• Reusable reference architectures and solution patterns for strategic industries and recurring client needs.
• Greater technical credibility with clients and Databricks field teams during strategic pursuits.
• Improved delivery velocity and quality through governed AI-assisted engineering practices.
• A growing bench of skilled and certified Databricks architects and engineers with clear development paths.
• Measurable reduction in avoidable technical risk, rework, and inconsistency across Databricks solutions.
• Visible technical thought leadership that strengthens the organization’s position in the Databricks ecosystem.

Qualifications

• 12+ years of experience in data engineering, platform engineering, solutions architecture, analytics engineering, or related technical disciplines.
• Demonstrated senior-level technical leadership across enterprise data platforms, with meaningful responsibility for architecture quality, standards, and team development.
• Deep hands-on Databricks experience, including production implementations rather than certification-only knowledge.
• Expertise in modern lakehouse architecture, data engineering, governance, analytics, AI/ML platform patterns, performance optimization, and cloud security.
• Experience designing and delivering enterprise-scale solutions across Azure, AWS, and/or Google Cloud.
• Strong consulting or professional services experience, including discovery, estimation, solution shaping, staffing considerations, delivery risk, and client dynamics.
• Proven pre-sales experience with executive presentations, architecture workshops, technical proposals, and complex solution design.
• Experience leading, mentoring, or managing senior architects and engineers.
• Executive presence and the ability to communicate complex architecture concepts to both C-level stakeholders and hands-on engineering teams.
• Strong written communication, technical storytelling, facilitation, and stakeholder influence skills.
• Travel up to 50%, primarily within North America.
• Preference is for candidates located within commuting distance of one of Perficient's North American markets.

Preferred Qualifications

• Databricks Professional certification, Databricks Champion recognition, or comparable evidence of advanced platform expertise.
• Experience with Mosaic AI, multi-agent orchestration, RAG, model evaluation, Vector Search, MLOps, LLMOps, and production AI systems.
• Proficient in leveraging AI technologies to drive innovation, support strategic initiatives, and enable data-driven decision-making.
• Possesses a strong understanding of AI capabilities, limitations, and ethical considerations.
• Experience establishing or scaling a technical Center of Excellence, platform practice, or architecture community.
• Industry depth in healthcare and life sciences, financial services, retail, manufacturing, automotive, energy, or utilities.
• Multi-cloud Databricks delivery experience.
• Conference presentations, technical publications, blogs, podcasts, community leadership, or other visible thought leadership.
• Experience collaborating directly with Databricks field, alliance, product, or engineering organizations.