90%+
reduction in manual effort for recurring finance and revenue reporting
Calgary, Alberta
Paul Taiwo-Adeyemo
Analytics Engineer | Data Engineer | BI & Analytics | Educator
I build data pipelines, reporting systems, and forecasts for finance and operations teams.
Analytics engineer with 5+ years in fintech payments. I work on dbt models, KPI definitions, executive dashboards, and forecasting at Paramount Commerce, and I teach data analytics at SAIT.
Analytics cockpit
Revenue · Pipelines · Cost
Forecast accuracy
97.4%
validation window
Manual reporting
-90%
finance workflows
Report load time
-85%
critical Tableau
Revenue forecastbacktest 98%
dbt run finance_martssuccess
vertex forecast refreshsuccess
bigquery slot watchwatching
Tools in daily use
PythonSQLdbtBigQueryLookerLookMLTableauGCPAirflowVertex AI
About
Analytics engineer and instructor in Calgary.
I work in data engineering, analytics, and machine learning. Before that I worked in biotechnology and scientific research.
At Paramount Commerce I build pipelines, forecasts, and BI reporting. At SAIT I teach SQL, Python, R, modeling, machine learning, and analytics engineering.
More about meImpact
Selected results
Numbers from finance reporting, forecasting, and BI work at Paramount.
97-98%
forecasting accuracy on revenue forecasting and backtesting
85%+
faster load times for critical Tableau reporting
30%
reduction in Tableau Cloud storage usage
200+
reports managed across BI environments
45%
reduction in BI licensing costs
Projects
Featured projects
Forecasting, reporting automation, Looker migration, and cost monitoring.
Forecasting
Revenue Forecasting Platform
A forecasting pipeline that gives finance and business teams reliable forward-looking revenue estimates.
PythonBigQuerydbtVertex AI
Automation
Automated Finance Reporting
Automated recurring finance and revenue reporting that used to be assembled by hand.
SQLPythonBICloud Data Warehouse
Platform
Tableau to Looker Modernization
Business reporting on BigQuery, dbt, LookML, and Looker.
BigQuerydbtLookMLLooker
Observability
BigQuery Cost Observability
Monitoring for expensive queries, heavy users, and cloud spend so cost issues surface earlier.
BigQueryGrafanaDatadogCloud monitoring
Current work
Current roles
October 2022 to Present
Paramount Commerce
Data Engineer II, Analytics Engineering
Building governed data models, semantic layers, forecasting systems, and BI for a Canadian fintech and payments company.
May 2023 to Present
Southern Alberta Institute of Technology (SAIT)
Adjunct Instructor, Data Science and Analytics
I teach data analytics and data science courses at SAIT, including SQL, Python, forecasting, and BI.
Modern analytics platform
AWS toward GCP, BigQuery, dbt, and Looker, with reusable models and governed reporting.
Finance automation
Recurring finance and revenue reporting with more than 90% less manual effort.
Revenue forecasting
Python, BigQuery, dbt, and Vertex AI. About 98% backtesting accuracy and 97% thereafter.
Banking and merchant analytics
Scorecards, performance tiers, FX ingestion, risk thresholds, and segmentation.
Data observability
Monitoring for expensive BigQuery workloads, pipeline reliability, and cloud spend.
BI optimization
Tableau administration, API automation, storage, licensing, and Looker migration.
Analytics architecture
Sources
Finance, payments, ops
BigQuery
Warehouse
dbt
Staging → Intermediate → Marts
LookML
Semantic layer
Looker
Governed BI
TestingDocumentationLineageKPI governance
Expertise
Technical skills
Analytics engineering, warehousing, BI, and machine learning.
Analytics Engineering
dbt · Advanced SQL · Dimensional Modeling · Data Marts · Semantic Layers · Data Testing · Documentation · Lineage
Data Engineering
ELT / ETL · Airflow · Data Pipelines · Data Warehousing · Data Quality · Kafka · CI/CD · Salesforce · HubSpot · NetSuite
Cloud and Warehousing
Google Cloud Platform · BigQuery · AWS · Snowflake · Redshift
Programming
Python · SQL · R · T-SQL
Business Intelligence
Looker · LookML · Tableau · Metabase · Power BI · QuickSight · Grafana
Machine Learning
scikit-learn · XGBoost · Regression · Forecasting · Clustering · Classification · Vertex AI
How I work
How I work
Reliability
I add tests, documentation, and monitoring so other people can run and change the work.
Automation
If a report or process runs on a fixed schedule, I try to take the manual steps out of it.
Impact
I check whether a pipeline or dashboard actually changed how a team reports or decides.
Documentation
I write down metric definitions and model logic, and I teach the same ideas at SAIT.
Business context
I start from the finance, risk, or operations question, then pick the simplest setup that answers it.
Journey
Background
My career began in biotechnology and scientific research, across laboratory work, bioinformatics, biologics manufacturing, and production environments.
Lab and manufacturing work trained me to test assumptions and repeat results. I later moved into data science, then data engineering and analytics engineering.
Career journeyWriting
Writing
2026-03-12
What I mean by analytics engineering
How I keep KPI definitions in dbt and LookML instead of burying them in workbooks.
2026-01-20
Moving from biotechnology into data engineering
How lab and manufacturing work carried over into pipelines, forecasts, and reporting.
2025-11-04
How I structure dbt projects from staging to marts
Staging, intermediate, and marts, with KPI definitions in the semantic layer.
Contact