Funmi Lawal

About Funmi Lawal

I am a Cloud and Data Engineer with over 12 years of experience designing and delivering scalable data pipelines, ETL workflows, and cloud architectures across AWS, Azure, and hybrid environments. My work spans both financial services and healthcare, where I’ve helped organizations transform raw data into secure, actionable insights that drive smarter decision-making.My expertise includes PySpark, SQL, Airflow, Terraform, and Databricks, with proven success in building real-time streaming pipelines, cloud migrations, and compliance-driven reporting solutions. I have automated complex workflows, reduced operational overhead, and accelerated analytics delivery for enterprise clients.Certified as an AWS Solutions Architect, DevOps Engineer, and Big Data Specialist, I bring a strong blend of technical depth and business understanding, ensuring that every solution is not only reliable and secure but also aligned with organizational goals.I’m passionate about cloud transformation, data quality, and enabling business intelligence at scale. Always open to connecting with data and cloud professionals, sharing knowledge, and exploring opportunities where I can help organizations achieve more with their data.

Experience

1. Cloud Data Engineer

   Capgemini

   Oct 2021 — Present

   Architected AWS cloud infrastructure (EC2, S3, Lambda, Kinesis, Glue, Athena) across high-availability environments, sustaining 99.95% uptime for 10+ enterprise workloads processing 5TB+ data daily.– • Designed federated data models and analytics-ready datasets in Big Query to support downstream consumers including clinical research teams, population health analytics, financial operations, and executive reporting.• Implemented data governance frameworks leveraging Google Cloud Data plex and Data Catalog to establish data lineage, metadata management, and data stewardship practices across enterprise healthcare data assets.• Developed automated data quality validation frameworks to perform data profiling, anomaly detection, and SLA monitoring, ensuring accuracy and reliability of healthcare data used for regulatory reporting and clinical decision support.• Developed complex BigQuery SQL transformations to support data modeling, aggregation, and analytical workloads, optimizing query performance through partitioning, clustering, and materialized views.• Implemented batch and near real-time ingestion pipelines using Pub/Sub and Dataflow, enabling reliable data streaming and integration from multiple enterprise systems including APIs, databases, and cloud storage.• Designed bronze, silver, and gold data layers within the GCP data lake to support governed, analytics-ready datasets for business intelligence and machine learning use cases.

Education

• Stratford University

  Master's degree, Health/Health Care Administration/Management

• George Mason University

  Engineer's degree, Data Processing and Data Processing Technology/Technician

• Babcock University

  Bachelor's degree, Microbiological Sciences and Immunology