Brian Baguma

About Brian Baguma

I engineer reliable data infrastructure and ensure data integrity across the entire pipeline from ingestion to analytics.With 6+ years building scalable systems across cloud platforms (AWS, Azure, GCP), I specialize in ETL development, data pipeline orchestration, SQL/Python automation, and end-to-end data quality assurance. My background spans full-stack engineering, cloud data solutions, and QA processes, giving me a unique ability to design, test, and validate data workflows that teams trust.WHAT I BRING TO DATA ENGINEERING & QA TEAMS: ETL & Data Pipeline Development – Design and deploy production-grade pipelines using Python, SQL, Apache Spark, and cloud-native tools (Azure Data Factory, AWS Glue, Airflow). Built systems processing millions of records daily with 99.9% uptime. Data Quality & Validation – Implement automated testing frameworks for data accuracy, completeness, and consistency. Create reusable validation scripts, anomaly detection logic, and reconciliation processes to catch issues before they reach production. SQL & Database Optimization – Expert in writing complex queries, stored procedures, and performance tuning across PostgreSQL, MySQL, Oracle, Snowflake, and MongoDB. Reduced query execution time by 60% through indexing and partitioning strategies. Cloud Data Warehousing – Hands-on with Snowflake, Azure Synapse, Redshift, and BigQuery for building dimensional models, star schemas, and optimized ELT workflows. Migrated on-prem systems to cloud, cutting storage costs by 40%. Test Automation & QA Processes – Built end-to-end test suites using Pytest, Great Expectations, and custom frameworks. Validate data transformations, API integrations, and ETL jobs with automated regression and smoke tests. CI/CD & DevOps for Data – Set up automated pipelines with Jenkins, GitHub Actions, and Azure DevOps. Version-control data workflows, enforce code reviews, and deploy with Docker/Kubernetes for consistent environments.RECENT IMPACT- Delivered scalable APIs and data dashboards for AI training platforms handling 10M+ data points- Designed cloud migration strategies reducing infrastructure costs by 35%- Automated data validation workflows, cutting QA cycle time from 5 days to 8 hoursAWS Certified Developer-Associate | Proficient in Agile/Scrum | Strong collaborator across product, analytics, and ML teams OPEN TO: Full-time W2 | Contract (Corp-to-Corp or 1099)| Remote or HybridLet\'s connect if you\'re hiring for ETL Developers, Data Engineers, or QA/Data QA Engineers who deliver data you can rely on.

Experience

1. Data Engineer

   Kforce Inc

   Oct 2023 — Present · Dallas, TX, US

   Architected and deployed ETL pipelines ingesting 10M+ annotation records daily using Python, Node.js, and GraphQL APIs, enabling real-time AI model training with <2% data lossDesigned automated data quality validation framework using Jest and custom Python scripts to validate schema integrity, detect anomalies, and ensure 99.8% data accuracy before downstream consumptionBuilt RESTful and GraphQL APIs for data ingestion, transformation, and retrieval across distributed systems; optimized query performance by 45% through caching and pagination strategiesDeveloped real-time analytics dashboards visualizing data quality metrics, pipeline health, and SLA compliance using React and D3.js, reducing incident response time by 60%Implemented end-to-end testing for data workflows using Playwright and Cypress, automating regression tests for ETL jobs and catching 40+ critical bugs before production release

Education

• Maharishi International University

  Masters, Data Engineering