A 360° senior profile · Same person, every layer
AI/Data · Backend · Big transactional databases · Frontend · Architecture
Principal-level architect and senior consultant. 34 years building enterprise systems — the last five leading production AI delivery, having built the Data & AI practice of a 200-engineer consultancy from zero to 25 engineers as its principal architect.
Personal code-level depth in AI/Data programming, Java/Python/Node.js backend, big transactional databases, and enterprise JavaScript/frontend. Frontend depth means complex, data-heavy enterprise web applications, UI architecture, framework-level work, Node.js tooling and long-lived products that have to survive production. After three decades, my job is no longer typing code in every language — it's making the architectural, technical and commercial decisions that decide whether a system ships. The same person designs the architecture, reviews the code, writes the SOW, stands in front of the client in late-stage sales, and answers for the outcome.
Three decades of overlapping disciplines — engineering depth, architecture, and the commercial side of delivery — carried by the same person. Below: where the technical expertise is, and where the senior reach extends beyond engineering.
AI/Data programming, Python, Java, Node.Js ETL and db languages programming, RAG and agentic systems. Multi-terabyte zero-downtime migrations on big transactional databases and cross-DB depth across relational.
Full Dataiku certification suite. Microsoft Fabric, Snowflake, Databricks. RAG, agentic systems, Azure OpenAI, LangChain. Production-grade AI systems delivered for enterprise.
Data-heavy enterprise web applications, UI architecture, JavaScript, Node.js tooling, grids, charts, accessibility, IDE integrations and long-running frontend products. Framework-level depth, including formal MVP recognition in the Sencha ecosystem.
Production-grade architecture for AI, data and platform systems. Reference architectures, Centers of Excellence, distributed systems at enterprise scale. Principal architect of a 200-engineer organization.
Brought in when others can't crack it. 34 years getting into mission-critical code to diagnose root causes and fix what's blocking production — elusive bugs, performance collapses, integration failures, data corruption. Code that thousands of users depend on.
Beyond pure engineering is not a separate offering. It is the same technical authority extending into the activities that surround serious software work: diagnosis, commercial framing, client trust, delivery control and organizational execution.
Codebase audits, organizational diagnostics, and confidential executive reports. I translate technical reality into board-level decisions when reality and strategy diverge.
22 years writing winning proposals and SOWs — including for third-party consultancies. I know the commercial cycle from the inside and the technical realities each clause carries.
Hired by clients to keep providers honest: code review, risk discovery, verification phases, distinguishing provider interests from client interests.
Built the Data & AI practice from zero to 25 engineers. Designed hiring plans, training programs, quality and cost-control processes.
Useful when credibility is the obstacle: late-stage sales, difficult client situations, project recovery, or teams that need a senior authority they can believe. The trust comes from seniority and demonstrable experience first — 34 years of shipped systems, hard technical work and real delivery responsibility — then gets reinforced when I can defend the architecture, inspect the code, answer hard questions and carry the outcome. Engineers tend to accept the authority because it is grounded in the work, not the title.
The compact version of the career path: companies, roles, periods and the responsibility carried in each stage. The detailed project-level history remains separate.
Lead AI & Data Architect · Principal Architect
Built the Data & AI practice from zero to 25 engineers inside a 200-engineer consultancy. Principal technical authority across delivery, architecture, pre-sales, SOWs, executive conversations and production AI programs.
Founder · CTO · Chief Architect · Software Developer
Ran my own software consultancy for 22 years. Full-cycle ownership across custom enterprise systems, BI, database engineering, enterprise web applications, client delivery, proposals, architecture and long-running client relationships.
Software Architect · Consultant
Recruited by a former INTECSA CEO to collaborate in the creation of a new engineering and software company, bringing technical architecture and delivery capability into the early business.
Project Manager · Architect · Software Developer
Owned technical delivery for civil-engineering, GIS, document-management, multimedia and enterprise management systems. Combined architecture, project responsibility, client interaction and hands-on implementation.
Software Analyst · Software Developer
Moved from implementation into broader technical responsibility: modernizing practices, owning technical aspects of projects, and representing the department in client and group-management meetings.
Analyst · Software Developer
Worked across organic and functional analysis, solo development and collaboration with the development department's analysts and director. Built early systems that became templates for later applications.
Software Developer
Started inside company informatization projects covering commercial and financial systems, participating in analysis, design and rollout of core business modules.
Projects, consulting and interventions for 100+ clients across industries — from full delivery ownership and architecture audits to data strategy and senior advisory. A representative selection below.
Two patterns that recur across decades — one technical-deep, one commercial-deep — both carried by a single Principal-level point of accountability.
A Fortune 500 industrial multinational engaged me to investigate why two strategic enterprise applications were causing recurring delivery problems. I studied the entire codebases personally and authored a confidential consultancy report for executive leadership. Then I interviewed directors and engineering teams to diagnose what was happening organizationally, and authored a second report.
A 200-engineer consultancy repeatedly brought me into final-stage commercial conversations as their technical trust anchor — public speaker at the Snowflake Summit in San Francisco, host at corporate events and online client sessions. Late-stage enterprise prospects — including Fortune 500 organizations engaging on Data & AI programs — committed to engagements after reviewing my profile and accepting me as the project's delivery owner and trusted technical guarantor.
The technical core behind the profile. Five overlapping disciplines, built up across three decades of hands-on work. Most engagements draw on more than one.
AI/Data programming first — Python, ETL pipelines, RAG and agentic systems. Backend depth across Java and Node.js. Deep big transactional database expertise: partitioning, optimization, zero-downtime production migrations and mission-critical systems at multi-terabyte scale. Cross-DB expertise across relational, NoSQL, vector databases and OpenSearch.
The depth most modern architects don't have. The kind that decides whether a system survives production load.
Built and led a 25-engineer Data & AI engineering organization end-to-end — strategic plan, hiring, training, SOW authorship, pre-sales, delivery, evangelism at industry events.
RAG and agentic systems. LangChain, Azure OpenAI, GPT-4o realtime. Data platforms: MS Fabric, Snowflake, Databricks, Dataiku (full certification suite). Reference architectures, Centers of Excellence, production-grade implementations.
The work that happens before code is written: understanding the problem space, existing system, domain, team and constraints well enough to know whether the requested solution is convenient, necessary, or even the right problem. Multi-year technical strategy, organizational analysis, codebase audits, architecture reviews shippable to investors and steering committees, SOW design and pre-sales engineering.
Strengthening high-stakes projects. Putting troubled ones back in order. Reducing risk and protecting quality by connecting the solution decision back to the code that will carry it.
Large, data-heavy enterprise web applications. UI architecture, JavaScript, Node.js tooling, grids, charts, accessibility, IDE integrations and frontend maintainability in long-lived products.
Framework-level depth, including vendor MVP recognition in the Sencha ecosystem, used as evidence of technical depth rather than as the market positioning.
Embedded as the technical lead for complex AI, Data or platform programs. Set technical direction, write reference designs, review code, lead pre-sales, present at client events.
Five years leading 25 engineers across Canada, US, India and Europe — async-first, English-default, accountable for delivery across backend, data, AI and frontend tracks.
A deliberately partial selection — grouped by domain rather than chronology. Many engagements are bound by NDAs or omitted to keep this readable. Full project history available here.
Five years architecting, leading and growing a fully remote Data & AI engineering organization — with engineers in Canada, the US, India and Europe. Three layers of work in parallel: strategic, architectural, and delivery. The clearest signal of how I operate when given full ownership.
A global technology consultancy needed a Data & AI division capable of meeting surging market demand and securing a leadership position. The brief wasn't a roadmap — it was a vacuum: build the entire technical, commercial and operational engine from zero, distributed across four geographies and time zones, while continuing to deliver to live clients.
Authored the multi-year strategic plan that aligned the division with company-level business objectives. Conducted formal organizational and resource analysis identifying gaps and risks, and authored the hiring plan that secured executive approval. Framed the division as a risk-mitigation and revenue-growth play, not a cost line.
Authored reference architectures and Centers of Excellence around Dataiku, MS Fabric, Snowflake, Databricks and Azure OpenAI. Designed the organizational blueprint of the division — roles, teams, responsibilities, internal processes. Defined the engineering practices, technical onboarding, and training programs that brought the team to production speed.
Led pre-sales daily with the commercial org; personally architected, wrote, and signed off on every Statement of Work that left the division. Represented the division at industry events as technical evangelist. Led high-stakes client workshops at executive level and translated complex technical proposals for non-technical audiences.
Final accountability for the delivery pipeline, profit, and quality of every project that left the division. Stayed in the codebase throughout: design reviews, key implementations, code review on the critical pieces. The same person who signed the SOW reviewed the production code.
The division became a primary engine for revenue growth and strategic positioning — measured not in slideware but in shipped, profitable engagements with clients who came back. The team I built, the plans I wrote, and the architectures I authored continue to operate the organization without me in the room.
Recruited, trained and mentored from a standing start — Canada, US, India, Europe.
Dataiku · MS Fabric · Snowflake · Databricks — each with mentored leads and shipping POCs.
Every technical proposal and SOW that left the division. No exceptions.
Fortune 500 industrials and global enterprise software vendors — high-profile, high-margin, repeat engagements.
Five operating principles. They explain why I'll sometimes turn down work — and why the engagements I take, I take seriously.
The right architecture comes from two readings: the software as it really is, and the problem it has to solve.
I still write code, run migrations, and review PRs. Architecture decisions land better when they're grounded in the codebase, not in a Visio diagram.
I need to understand the domain, existing system, team and constraints before I can responsibly design software for them. Sometimes that means the whole business; often it means one difficult project or workflow.
Clients do not need polite agreement when a decision will create risk. I discuss trade-offs directly, in client language and technical detail, until the choice is defensible.
Reference design, key implementations, code review, delivery decisions and the conversation with leadership — same person, same accountability, until the work ships.
Short client list on purpose. The team I'm embedded with gets full attention or none of it.
Open to engagements with clients worldwide — primarily North America and Europe. Fully remote, in English. Direct intros from engineering directors, CTOs, recruiters and consultancies welcome.
Best fit: complex AI, Data or platform programs where the team needs a Principal-level senior who can own architecture, inspect code, challenge assumptions, and still handle the client, sales, leadership and delivery conversation.
Engagement shapes are flexible: project rescue, embedded principal architect, technical due diligence, pre-sales & SOW support, executive technical advisory. Clients I've served range from PE-backed scale-ups to global enterprises across industrials, energy, financial services and enterprise software.
If your need doesn't fit a clean category — reach out anyway. I've been useful to clients in ways they didn't expect. Contact is exclusively through LinkedIn.
Send a direct message or introduction through my LinkedIn profile.
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