ByteElevateCase studies from the part of data work that has to hold up in production.
Architecture, migration, governance, data quality, and applied modeling. The common thread is not a tool. It is making the system more dependable and the decision easier to defend.
The problem
The ecommerce organization needed a data platform built from the ground up—one that could support product and analytics questions across large, heterogeneous datasets while fitting into the wider enterprise architecture.
The work
I led the platform architecture: choosing tools, working with vendors and engineering architecture teams, running proofs of concept, defining data interactions, and leading the team that built the platform.
- Designed the cloud-native big-data platform on AWS.
- Integrated clickstream, customer-data, experimentation, monitoring, and BI tooling.
- Led a production migration involving multi-petabyte heterogeneous data sources.
- Completed the migration without disrupting reporting.
- Later worked across engineering, product, and DevOps to plan migration of critical customer-experience resources from AWS to GCP.
The problem
As the company added customers and expanded its products, ingestion and transformation work became harder to scale consistently. New customer onboarding required significant cleaning, transformation, validation, and recurring updates.
The work
The goal was a practical single source of truth and a more reproducible operating model—not governance as a documentation exercise.
- Created a data catalog with a data dictionary, ERDs, lineage context, and links to source repositories.
- Standardized metadata creation and clarified data ownership.
- Created reusable ETL scripts to reduce repeated transformation work and improve maintainability.
- Added granular data-quality tests and alerts that could catch discrepancies earlier.
- Automated validation that had previously depended on manual post-project checks.
The problem
The client wanted to identify visitors who were likely to purchase but had not yet converted. Their existing method assigned subjective weights to behaviors such as product views, list creation, and bid placement.
The work
We used historical behavior and sales outcomes to identify which signals were actually predictive.
- Prepared a year of behavioral and sales data.
- Handled missing data, outliers, correlated behaviors, and class imbalance.
- Used regression-based predictive modeling to identify meaningful behaviors.
- Compared candidate models using AIC, AUC-ROC, and confusion matrices.
- Validated the selected model against more recent data.