Principal Engineer & Solution Architect- Data & AI | Azure Databricks | Financial Services

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I am the person teams call when a Databricks program is technically promising but operationally fragile.

I design and deliver data and AI platforms that survive real-world pressure: architecture board scrutiny, security controls, audit evidence requests, and production support at 2am. I have built systems for major institutions including NatWest, and I care deeply about making high-stakes platforms dependable, explainable, and repeatable.

I am hands-on in delivery, not just design. I work directly in Databricks, Python, SQL, and Terraform to translate architecture into working pipelines, governed data products, and deployable infrastructure.

My style is practical and transparent. I work closely with architecture, engineering, risk, and control teams to turn complex requirements into clear decisions and shippable delivery plans.

Personal principle: make production boring, because boring is what trust looks like in regulated environments.

• Faster architecture sign-off with clear, documented trade-offs.
• Stronger governance posture through explicit controls and ownership.
• Reduced platform drift via repeatable Terraform and delivery patterns.
• Better audit readiness through evidence-oriented architecture artifacts.

I typically support one of three engagement types:

• Architecture and readiness assessment: establish gaps, target state, and a delivery-ready roadmap.
• Solution architecture pack delivery: produce full review-ready artifacts (vision, ADRs, NFRs, controls, operating model, readiness).
• Platform delivery acceleration: convert architecture into implementation across data, infra, and operations, including direct engineering support.

The project below is presented as a practical case study: business problem, architecture approach, and business relevance.

• Repository: fraud-detection-platform
• Problem solved: fragmented fraud data pipelines created slow investigation cycles and weak governance traceability.
• Architecture approach: governed Bronze/Silver/Gold model with explicit control boundaries and ADR-led decision flow.
• Why it matters: shows architecture-to-delivery leadership for a high-scrutiny fraud domain.

My architecture decisions are rooted in established frameworks, then adapted for real Databricks delivery. The principles I use are informed by the Well-Architected approach (reliability, security, operational excellence), DAMA-DMBOK (data governance and stewardship discipline), and TOGAF (architecture structure, traceability, and decision rigor).

I wrote my principles this way on purpose: they are practical enough for engineering teams to apply day-to-day, but structured enough for architecture, risk, and audit stakeholders to trust. They help me make trade-offs explicit and consistent across platform, data, controls, and operating model decisions.

This is the foundation I return to on every engagement: principles.

Over years of delivering in high-scrutiny environments, I have refined a structured artifact set that turns principle-led architecture into execution. These artifacts are how I move teams from ambiguity to accountable delivery: vision, ADRs, NFRs, control architecture, operating model, risk register, readiness checks, and evidence planning.

This is the pack I use to keep work review-ready and implementation-focused: solution_architecture_templates.

• I make ownership explicit so control failures are visible and actionable.
• I document key decisions early to keep delivery aligned.
• I build evidence into day-to-day delivery, not as a last-minute audit task.
• I prioritize repeatable operating models over one-off heroics.

If you are scaling Azure Databricks in a regulated environment, I can help with:

• defining target architecture and getting it review-ready for architecture, security, and risk forums
• embedding governance and controls directly into platform and data workflows
• accelerating implementation through hands-on Databricks, SQL, Python, and Terraform delivery
• aligning evidence and assurance so high-scrutiny delivery remains defensible in production

• LinkedIn: paulkarikari