Fractional Data Engineer & AI Infrastructure Engineer
Accepting new clients · Remote · Async-friendly

Fractional Data &
AI Infrastructure
Engineer.

I help teams build reliable data pipelines, optimize Spark workloads, and deliver the infrastructure that makes AI and GenAI actually work in production — without hiring a full-time engineer.

Whether you're building your first AI use case or scaling existing systems — the foundation is the same.

Book a Discovery Call → Explore Services ↓
10+ Years Experience
3 Cloud Platforms
5 DE + AI Services
Databricks
Apache Spark
Delta Lake
RAG Pipelines
Apache Kafka
Vector Search
Streaming Pipelines
CDC Pipelines
Lakehouse Architecture
Agentic AI
AWS
Azure
GCP
Python
PySpark
AI Agents
Workflow Automation
Databricks
Apache Spark
Delta Lake
RAG Pipelines
Apache Kafka
Vector Search
Streaming Pipelines
CDC Pipelines
Lakehouse Architecture
Agentic AI
AWS
Azure
GCP
Python
PySpark
AI Agents
Workflow Automation
About

Data engineering expertise,
extended for the AI era.

I'm a senior data engineer with 10+ years of experience building production-grade data platforms across regulated industries and high-scale environments.

As AI moved from research to production, I extended my practice to cover the full stack — from Lakehouse architecture on Databricks and real-time Kafka pipelines, to RAG systems, and AI-ready data infrastructure.

My focus is the data and infrastructure layer — not model development. I build the pipelines, retrieval systems, and platform foundations that make your AI initiatives actually work in production. The models are yours; I make sure the data behind them is reliable.

Industries
Banking Insurance Telecom eCommerce Gaming
DE
Databricks & Spark Platforms Expert-level design, tuning, and optimization of Databricks environments and Spark workloads at scale.
DE
Scalable Data Pipelines Production-grade batch and streaming pipelines — reliable, observable, and maintainable.
AI
AI-Ready Data Infrastructure Structuring data platforms for AI/GenAI use cases — feature pipelines, Delta Lake modeling, data quality, and embedding pipelines.
RAG
RAG Infrastructure & LLMOps Vector search setup, retrieval pipeline design, MLflow-based pipeline observability, and AI data governance via Unity Catalog.
OBS
Platform Observability Monitoring, alerting, data lineage, and SLA tracking — from pipeline to AI inference layer.
Common Challenges

Where data teams get stuck —
and AI projects stall.

01 / 08
Slow or Failing Spark Jobs
Pipelines that run unpredictably, timeout under load, or fail silently — costing hours of debugging time.
02 / 08
Runaway Cloud Costs
Over-provisioned clusters, inefficient compute usage, and no visibility into what's driving Databricks spend.
03 / 08
Messy Data Lakes
Poorly structured datasets, no schema enforcement, and data quality issues that cascade into unreliable analytics — and untrustworthy AI outputs.
04 / 08
No Monitoring or Observability
Pipeline failures discovered by end users — no lineage, no SLA tracking, no visibility into data freshness or model drift.
05 / 08
Streaming & CDC Gaps
Batch-based pipelines that can't fulfill real-time ingestion needs — blocking low-latency AI and analytics use cases.
06 / 08
Legacy ETL Debt
Fragile, undocumented ETL workflows that are expensive to maintain and block the team from modernizing.
07 / 08
AI Projects Stalled on Data Quality
Models are ready. Use cases are defined. But inconsistent, ungoverned, or unstructured data means AI outputs can't be trusted in production.
08 / 08
Manual Workflows AI Could Automate
Lead qualification, reporting, data routing, customer interactions — repetitive processes consuming your team's time that AI agents could handle reliably, but no one knows where to start.
Services & Pricing

The full stack — from data pipelines
to production AI.

◈ Data Engineering Services
Fixed Scope
Databricks Optimization Audit
A focused engagement to identify performance bottlenecks and cost inefficiencies in your Databricks environment — with a clear action plan.
  • Spark job performance analysis
  • Delta Lake table optimization
  • Cluster configuration review
  • Cost optimization opportunities
$6K – $12K
DURATION: 1–3 WEEKS
Most Popular
Fractional Data & AI Engineer
Part-time senior support — ongoing, hands-on, embedded collaboration across your data platform and AI infrastructure.
  • Build & optimize Spark pipelines
  • Stabilize unreliable workflows
  • Implement streaming & CDC pipelines
  • AI infrastructure ownership
  • Mentor internal engineers
$10K / mo
21 HRS/WEEK · MIN. 4 MONTHS
Project-Based
Data Platform Modernization
End-to-end migration from legacy data systems to modern, scalable platforms — designed for AI readiness and operated by your team.
  • Migrate ETL workflows to Databricks
  • Implement Lakehouse architecture
  • Design scalable data pipelines
  • Governance, reliability & AI-readiness
Pricing based on scope
⬡ AI and Business Automation Services
GenAI
Internal AI Assistant (RAG)
Build the data and retrieval infrastructure that powers AI assistants — so your team can query internal documents, systems, and knowledge bases reliably.
  • Document & data ingestion pipelines
  • Embedding & vector search setup
  • RAG architecture & retrieval design
  • API integration for your AI application layer
Pricing based on scope
AI Agents
Business Process Automation
Build reliable, production-ready AI agents that automate repetitive business workflows — from lead qualification to internal data routing — backed by solid data infrastructure.
  • From assessment through to production deployment
  • Scoped to your specific workflows
Pricing based on scope
How It Works

Flexible by design.
Senior by default.

Contact

Let's talk about your
data & AI infrastructure.

Ready to make your AI initiatives actually work?

Whether you're struggling with pipeline performance, evaluating an AI use case, or need ongoing data & AI infrastructure support — I'd love to hear about your situation.

Remote Available globally, fully remote
Async Async-first communication
Response Within 1 business day
Start Typical start within 1–2 weeks
⬡ Prefer to talk first?

Schedule a Discovery Call

30 minutes — no commitment. We discuss your data platform, AI goals, or automation challenges and figure out if there's a fit.

30 minutes
Google Meet / Zoom
Remote · Any timezone
WhatsApp