Helana
Nosratbakhsh
I build the data infrastructure that powers modern businesses — from real-time pipelines to cloud warehouses to ML platforms. Available for advisory engagements and fractional data leadership.
Building the infrastructure
data teams dream of
I'm a Senior Data Engineer with over 8 years of experience designing, building, and scaling enterprise-grade data platforms. My work spans healthcare, retail, and SaaS — industries where the cost of bad data is measured in real business outcomes.
I specialize in ERP and CRM integrations, Snowflake-native pipelines, and Data Vault 2.0 implementations — bridging the gap between complex business requirements and highly technical engineering. Whether that means restoring revenue reporting accuracy, migrating a legacy warehouse to the cloud, or building ML data infrastructure, I design for both immediate impact and long-term reliability.
Services
Data Pipeline Architecture
End-to-end design and implementation of batch and real-time data pipelines that scale with your business and won't become tomorrow's technical debt.
Cloud Data Warehouse
Build or modernize your data warehouse on Snowflake, BigQuery, or Redshift — with performance, governance, and cost efficiency from day one.
ML Data Infrastructure
Lay the data foundation your ML team needs: feature stores, training pipelines, and model serving infrastructure built for production reliability.
Analytics Engineering
Transform raw data into trusted, business-ready datasets using dbt, with full documentation, lineage, and automated testing your analysts can rely on.
My Stack
Case Studies
Real-Time Analytics Pipeline
Transformed a batch-processing analytics system into a real-time streaming pipeline, enabling business decisions on live data rather than day-old reports.
Cloud Data Warehouse Modernization
Migrated a decade-old on-premise SQL Server data warehouse to a modern cloud-native architecture, unlocking self-service analytics and cutting infrastructure costs by 60%.
ML Feature Store Platform
Built a centralized feature store that reduced model development time from weeks to days, while ensuring consistent feature computation between training and production serving.
From the Blog
Building a Scalable Data Lake on AWS: Lessons from the Trenches
After migrating three enterprise data warehouses to AWS S3-based data lakes, I've collected a set of principles that separate architectures that scale gracefully from the ones that quietly become technical debt.
dbt in Production: What Nobody Tells You
dbt has transformed how teams write and maintain SQL transformations. But the gap between a working dbt project and a production-ready one is wider than most teams realize.
Snowflake vs. BigQuery vs. Redshift in 2025: A Practitioner's Take
Every month someone asks me which cloud data warehouse they should use. The honest answer is: it depends — but here's a framework that actually helps you decide.
Ready to transform your
data infrastructure?
Whether you need a pipeline architected from scratch, a legacy system modernized, or strategic guidance on your data roadmap — let's talk.