I will consolidate multi department data into bigquery, snowflake, or ms fabric
Senior BI Specialist Executive Dashboards Cloud Analytics
About this Gig
To make fast executive decisions, you need your departmental data consolidated into a single, high-performance cloud data warehouse.
With 10 years of enterprise data analytics experience across FMCG, E-Commerce, and Healthcare, I build reliable data engineering pipelines and cloud data warehouse architectures using Google BigQuery, Snowflake, and MS Fabric.
What I Will Do For You:
- Multi-Source Data Consolidation: Combine raw data from Sales, Marketing, Operations, and Finance into a single cloud repository.
- Automated ETL/ELT Pipelines: Write clean, optimized SQL scripts and automated ingestion workflows to replace manual spreadsheet exports.
- Data Warehouse Architecture: Design scalable Star Schema / Snowflake Schema models tailored for fast reporting.
- Data Cleaning & Standardization: Remove duplicates, fix missing values, standardize date formats, and build error-free data models.
- BI Connection Readiness: Connect your new data warehouse directly to Power BI, Looker Studio, or Tableau for seamless reporting.
Tech Stack Expertise:
- Cloud Warehouses: Fabric, BigQuery, Snowflake
- Languages & Querying: SQL (PostgreSQL, BigQuery Standard SQL, T-SQL)
- Environments: GCP, Azure, MS Excel
FAQ
Which cloud warehouse should I choose (BigQuery, Snowflake, or MS Fabric)?
It depends on your current ecosystem. If your business relies heavily on Google Workspace and Looker, BigQuery is seamless. If you use Microsoft 365 and Power BI, MS Fabric is ideal. If you have high-volume multi-cloud data, Snowflake works best. I can help you decide during project kickoff.
Will you need admin access to my cloud accounts?
I only need temporary IAM/service account permissions restricted to the specific dataset or project folder we are working on. We can also set up access via a live screen-share session.
Can you clean messy Excel files before loading them into BigQuery/Snowflake?
Yes. Data cleaning, schema design, and column mapping are built into all standard and premium packages.

