Bassey Etok

Owned and rebuilt 9 dbt models on BigQuery for VMO2’s fibre network build reporting to meet central team engineering standards with byte-identical outputs for dependent Power BI dashboards. Built the Power BI report used by project leads, managers, and site engineers to track partner progress (civil/fibre), completed/overdue L4 cabinets, DFE submissions, and overrides vs Deepomatic AI. Re-layered models into staging, intermediate, and mart layers and repointed up to 8 downstream consumers without impacting reporting. Implemented rigorous change validation (EXCEPT DISTINCT diffs, row-level JSON comparisons, grain checks) and profiled sources to resolve root-cause issues including dropped joins, missing cabinet installation data, and missing coordinates. Merged a historical Excel tracker with new system feeds using agreed precedence rules and reconciliation checks. Added dbt tests and documentation, fixed mapping and duplicate-record logic issues, and optimised build queries by reducin

Built reporting tables for questions no existing tables could answer by locating relevant data across multiple sources and integrating it with applied business logic. Used Matillion to join/transform data from 10+ source tables into curated Snowflake reporting tables, deriving new fields and cutting preparation time by 20%, with reporting refreshed every 1–2 hours across 6 downstream teams. Extracted and flattened semi-structured JSON into Snowflake gold-layer tables for reporting usability. Designed data models from conceptual sketches to physical built tables (including star schemas and a central semantic layer) so executive and operational reporting used consistent KPI definitions. Added validation rules, reconciliation checks, and documented lineage/metadata for auditability. Acted as go-to person for incorrect collections metrics by tracing discrepancies back to sources and aligning KPI definitions with the business. Optimised 25 slow SQL queries on multi-million-row datasets, r

Built and maintained Tableau and Unidash dashboards for EMEA product analytics, providing 15+ product teams consistent views of DAU, retention, and engagement. Wrote SQL for ad-hoc stakeholder questions and produced new dashboards on demand. Used Python (pandas) for data cleaning and analysis. Optimised SQL on high-volume datasets, improving query performance by 15% and reducing reporting latency by 20%. Developed budget forecasts using prior-year actuals adjusted for under- or over-spend against forecast.