SELECTED WORK
Representative professional experience behind AyuBee Analytics, plus independent technical exercises that show our approach.
BUILT FOR TRUST AND SCALEWork across ingestion, dbt transformations, dimensional models and certified analytics marts supported marketing, growth, customer experience and operations with shared KPI definitions and self-service reporting.
DataHub ownership introduced metadata, lineage, documentation and quality controls, making datasets easier to find, understand and use.
Large-scale pipelines and dimensional models supported CRM, telesales, performance analytics, scoring, anomaly detection and fraud-related analysis, with automatically refreshed reporting.
ETL / ELT workflows, staging layers and fact tables were strengthened with alerts, reconciliation and anomaly checks, alongside performance investigation.
Python ingestion brought advertising API data through cloud storage into Snowflake. Staging, transformation and aggregate layers connected network data to internal ad performance reporting and a broad KPI tracker.
TECHNICAL PORTFOLIO
Independent interview exercises and architecture demonstrations. These were not commercial client projects or production systems for the organizations involved.
Warehouse models for account creation, closure and reopening, with a daily activity definition using status and a trailing seven-day transaction window.
SQL and Python analysis for advertiser performance, anomaly candidates, daily spend and revenue, plus an Android creative experiment.
A method for clarifying output and source semantics, validating malformed data, mapping failure modes and establishing tests and ownership.
A rate-aware Python extraction pattern through object storage and Snowflake, with idempotent loads, backfills, schema drift checks and lineage.
LET'S BUILD WHAT'S NEXT
Bring us the difficult pipeline, the missing metric, or the reporting nobody quite trusts.