Data systems you can actually maintain
I centralize your data, automate ingestion, and build pipelines your team can manage—without me. Clean infrastructure. Proper handoff. Built for operators, not just engineers.
What you get in the first 2 weeks:
Discovery doc mapping your data sources → Architecture plan showing the path forward → First pipeline running with your most critical data source
The problems I fix
Data scattered across 7+ tools
HubSpot, Shopify, QuickBooks, Google Sheets, your ERP... nobody knows which number is right. You need a single source of truth.
Manual exports eating 10+ hours/week
Your ops team is downloading CSVs, copying into spreadsheets, and emailing reports. This should be automated.
Dashboards showing yesterday's data
Or last week's. Or last month's. By the time you see a problem, it's already cost you.
Can't answer basic questions
"Why did revenue drop?" requires 3 days of investigation and 4 different tools. Leadership needs answers in minutes, not days.
Pipelines breaking silently
You find out when stakeholders ask "why is the dashboard empty?" You need monitoring and alerts that actually work.
Cloud costs growing faster than value
You're paying for full refreshes when incremental would work. Compute runs 24/7 when it only needs 2 hours/day. Right-sizing matters.
What I build for you
Not vague "solutions"—concrete deliverables your team can maintain
1 Centralized Data Warehouse
Snowflake, BigQuery, Databricks, or Postgres—whatever fits your scale. Schema designed for your questions, not just for storage.
2 Automated Pipelines
API connectors, incremental loads, CDC—no more manual exports. Data flows automatically from your tools into your warehouse.
3 dbt Transformation Models
Clean, tested, documented data models. Your analysts can understand and modify them. Version controlled with CI/CD.
4 Monitoring & Alerts
Tests that catch issues before stakeholders do. Alerts that go to Slack/email. Dashboards showing pipeline health.
5 Executive Dashboards
Power BI, Tableau, Looker—built on clean data. Leadership gets answers in seconds, not days.
6 Handoff Documentation
Runbooks, data dictionary, architecture diagrams, troubleshooting guides. Your team can maintain this without me.
I build systems you can maintain—without me
I'm not here to create dependency. I'm here to build clean infrastructure your team can own.
I don't overcomplicate things.
No unnecessary tools, no resume-driven architecture. If Postgres can handle your scale, I won't push you to Snowflake.
I build for operators, not just engineers.
Your RevOps, finance, and marketing teams need to use this data. I design for them, not for data scientists.
I hand off with confidence.
Documentation isn't an afterthought—it's a core deliverable. Your team gets runbooks, diagrams, and training.
Example solutions I've built
Scenario-based examples showing what's possible. Each represents real challenges I've solved.
Charter School Enrollment Analytics
HubSpot API integration revealing untouched leads. 22% reduction in missed follow-ups. Power BI dashboards across 10+ schools.
HubSpot, Python ETL, Power BI →
E-commerce Inventory & Demand
Shopify + warehouse data combined for automated restock alerts and demand forecasting. Reduced stockouts by 35%.
Shopify API, Snowflake, dbt, Looker
SaaS Revenue & Churn Analytics
Stripe + product usage data for cohort analysis, churn prediction, and expansion revenue opportunities.
Stripe API, BigQuery, dbt, ML models
Healthcare Clinic Operations
EMR + scheduling + billing data for patient flow optimization and revenue cycle analytics.
HL7/FHIR, Postgres, Power BI
Multi-Location Retail Performance
POS + inventory + staffing data unified for store-level performance comparisons and regional insights.
Square API, Snowflake, Tableau
Logistics Route Optimization
GPS tracking + order data for delivery time predictions and route efficiency analysis.
IoT APIs, TimescaleDB, Python ML
How the engagement works
Discovery
1-2 weeks
Map your data sources, identify bottlenecks, define success metrics. You get a discovery doc + architecture plan.
Foundation
2-3 weeks
Set up warehouse, automate first critical data sources, basic transformations, initial monitoring.
Expansion
2-4 weeks
Add remaining sources, build full transformation layer (dbt), create dashboards, advanced monitoring.
Handoff
1 week
Documentation delivery, team training, runbook creation, support plan. You're self-sufficient.
Outcomes I optimize for
Decisions on Real-Time Data
Not gut feel. Not stale reports. Real data flowing automatically.
10-40 Hours Saved Per Week
Typical range for manual export and reporting elimination.
Pipeline Uptime >99%
With monitoring and alerts that catch issues before users do.
Clear Data Lineage
Everyone knows where numbers come from and can trust them.
Teams Self-Serve Answers
Analysts and ops leaders can explore data without SQL experts.
Cost-Efficient Architecture
Right-sized for growth. No waste on over-provisioned infrastructure.
Ready to stop making decisions on stale data?
Book a 20-minute call. No sales pitch—just a conversation about your data challenges and whether I can help.