Data engineers had no reusable tooling to work with the existing ODCS data contracts, slowing down day-to-day work.

Built custom tooling for data engineers on top of the existing ODCS setup to streamline day-to-day work with data contracts.

Faster, more reliable day-to-day work with existing data contracts — tooling in active use, mission ongoing.

Trino and BigQuery use different SQL dialects, and no cross-repo mapping existed to identify dialect-specific patterns across the multi-repo data product architecture ahead of a migration to BigQuery.

Audited Trino-dialect SQL usage across the multi-repo data product architecture and formalized the migration approach in an ADR. Built a CLI to automate the dialect rewrite from Trino to BigQuery SQL, letting the data engineering team cut over each repo with a validated, ready-to-review rewrite.

A documented ADR grounding the migration approach, and a migration CLI automating the Trino-to-BigQuery dialect rewrite for the data engineering team to cut over repo by repo — mission ongoing.

Datasets lacked tracking, making data observability a blind spot for the team — and any solution needed to work across both Trino and BigQuery.

Built a dbt package for data observability that runs cross-database on both Trino and BigQuery, with each of the four pillars covered by design rather than bolted on afterward.

Dataset-level observability closing a previous blind spot, via a reusable cross-database dbt package — mission ongoing.

Raw GA4 exports were inconsistent and difficult to reuse across teams and industries, and BigQuery queries were inefficient with high, unpredictable costs.

Medallion architecture (bronze, silver, gold) with dbt and Dataform, with specialized marts for user behavior, product interactions, and simulations. Query refactoring with partitioning, clustering, and cost/governance monitoring. Data Studio dashboards for analytics delivery.

Unified GA4 models with reliable KPI tracking, delivered via Data Studio dashboards saving 6 hours per week. 30% lower average query cost and 2x faster queries across platforms.

Legacy Dataroma infrastructure was brittle and required migration to a modern platform. Multiple disparate data sources needed consolidation, and manual reporting was time-consuming with low confidence in the numbers.

Migrated to a Modern Data Stack as technical lead, with multi-source integration (Catchr, Couchdrop) and DSP extraction. Medallion architecture (bronze, silver, gold) with Kestra orchestration and Airbyte for source integration. Modular dbt models with marts layer, automated retries, error handling, and data quality testing.

Modern, scalable data platform with standardized, fully governed dbt practices. More than 5 hours saved per week for reporting, with 100% confidence restored in the numbers across net sales, bundle performance, upsell rates, and customer acquisition costs.

Data quality issues and dbt failures were detected too late, business teams lacked real-time KPI visibility, and multi-store dashboards needed secure, role-based access with monitoring for missing data.

Slack API integration with dbt and BigQuery for automated failure/error alerts, scheduled KPI reporting with thresholds, and interactive Slack commands for data access. Dashboard architecture redesign with Row-Level Security (RLS) for multi-store access and a consolidated global dashboard.

Faster failure detection through instant Slack notifications, scheduled KPI delivery in team channels, and secure, consolidated multi-store dashboards with automated monitoring across 5 industries.

Data quality received from retailers needed auditing and monitoring, and the dbt codebase needed refactoring to reduce query costs and improve performance.

Audited retailer data quality and implemented monitoring systems. Refactored the dbt codebase and implemented best practices for the data team.

Refactored dbt codebase with improved pipeline performance and reduced query costs.

Retailer reporting was manual — campaign selection, generation, and delivery to retailers all required repetitive hands-on work each cycle.

Built a Streamlit application automating retailer reporting: campaign selection, generation, and writing to Google Sheets via the Python API.

Streamlit application automating manual reporting, saving 5+ hours per week.