Wander · Data ETL

Contact

LinkedIn Email Me

Work Experience

Full-stack data with strong experience in SQL (Oracle), ETL automation, Power BI, data modelling, large-scale pipelines and analytics. Built end-to-end dashboards and ETL processes for datasets ranging from 100k to 1B+ rows, optimized Oracle SQL for sub-second performance, and engineered reproducible ETL workflows across telecom billing and Canadian open-data domains. Experienced in building custom ETL tools using SQL, PL/SQL, and shell scripting to automate batch jobs, manage partitions, and streamline high-volume data processing. Skilled at turning ambiguous questions into clean data models, measurement layers, and actionable insights.

Skills

Data Engineering & ETL

SQL (Oracle)PL/SQLETL pipelinesBatch automation Shell scriptingData quality checksHadoop / Hive (local cluster)

Analytics & Modelling

Power BIDAX measure layersData modelling (star schema) Time seriesSynthetic data generation (100k–10M rows) Exploratory data analysis

Tools & Languages

shellMonitoring scriptsHTML / CSS

Selected Projects

Calgary Civic & Housing Analytics Platform
38+ dashboards · property tax, housing, crime, schools, labour
  • Designed and built a multi-topic open-data platform for Calgary, covering housing, crime, education, public health, business survival, and migration.
  • Created star-schema models and DAX measure layers to reuse metrics across 38 Power BI pages.
  • Implemented monthly and yearly refresh pipelines using Oracle + shell scripts, with documented data dictionaries and governance notes.
Youth NGO Analytics — Community & Program Insights
Multi-page Power BI · 100k+ synthetic client & volunteer records
  • Built an end-to-end analytical solution for a Calgary youth mentoring NGO.
  • Designed cohort and engagement dashboards, geographic segmentation, and trauma / risk indicators.
  • Engineered a clean data model and reusable DAX measure layer to support multiple story-telling pages (clients, volunteers, matching, outcomes).
  • Produced presentation materials and a 5-minute video focused on insights and real-world program decisions.
Telecom — Yearly Peak Call Analysis (Hadoop / Hive)
Log ETL & performance tuning · historical peak in < 2 minutes
  • Originally, finding yearly peak traffic required ~30 person-days of manual Oracle work.
  • Built an automated pipeline: Hive DDL and partition loading, and a single query to compute yearly peak.
  • Reduced end-to-end processing to ~90 minutes (including transfer + load), with the final peak query running.
  • Designed the solution to be reproducible on a local Hadoop Hive cluster for future benchmarking.
Telecom — Incremental CDR Verification for Customer Call CDR
Partitioned CDR pipeline · ~30× scan reduction · revenue recovery
  • Re-designed the monthly customer call CDR check from a full-table scan into a daily incremental process using Oracle partitions.
  • Only scanned the previous day’s partition and marked records as “used” in a status table; end-of-month work handled only the small remaining set.
  • Eliminated query timeouts and reduced OLTP pressure by ~30× compared to the original approach.
  • Helped uncover and recover approximately $230,000 in fraudulent commission claims within the first month after deployment.

Experience

© 2025 YYC-Wander