I help teams modernize PySpark, Databricks, Azure/AWS, and compliance-heavy data platforms when reliability, auditability, and delivery speed all matter.
Each engagement is scoped around a concrete decision, delivery risk, or platform improvement, not generic advisory work.
Architecture, migration, tuning, and production hardening for large-scale Spark workloads.
For teams that already have data volume, operational pressure, or migration risk. I help design batch and streaming pipelines, reduce runtime and cost, and make PySpark systems easier to test, deploy, and operate.
Azure, AWS, and Databricks platform design for regulated data systems.
Practical platform design for data teams that need clear ownership, predictable delivery, and maintainable cloud infrastructure. The work can cover landing zones, data lake patterns, orchestration, CI/CD, observability, and operating models.
PII/PIPEDA-aware data handling, auditability, and role boundaries.
For healthcare, government, finance, and other regulated environments where data quality and compliance are part of the engineering work. I help teams make privacy and auditability part of the design instead of late-stage cleanup.
Hands-on guidance for teams shipping high-stakes data platforms.
Useful when a project needs senior engineering judgment without adding a full-time executive layer. I can support roadmap clarity, stakeholder translation, code and architecture reviews, hiring loops, and delivery risk reduction.
Reliable MLOps and AI-assisted engineering workflows for production teams.
I help teams use AI tools where they actually improve engineering throughput: data quality checks, developer workflows, model delivery, evaluation loops, and internal automation around existing systems.
Controls, monitoring, and evidence that make critical data systems easier to trust.
For teams that need stronger confidence in production data. I help define validation rules, pipeline health checks, release gates, lineage signals, and operational dashboards that make failures easier to find and explain.
The goal is to move from ambiguity to an executable technical path, then support the delivery work that proves it.
Clarify the business goal, data boundaries, current architecture, risks, owners, and operating constraints.
Review pipelines, platform choices, compliance needs, delivery workflow, and production failure modes.
Produce a concrete plan: architecture, tradeoffs, milestones, team responsibilities, and acceptance criteria.
Support implementation, review, testing, deployment, observability, and handoff so the result is maintainable.
The best fit is production data engineering work involving PySpark, Python, Databricks, Azure, AWS, regulated data, compliance constraints, or technical delivery leadership.
Yes. Many useful engagements start with an architecture review, performance review, migration plan, or production readiness assessment of an existing system.
Yes. My strongest experience is in regulated environments where reliability, auditability, PII handling, and stakeholder communication matter.
