Ahmed Oladapo

• • Architected and deployed a real-time CDC data pipeline using Google Cloud Datastream, replacing a fragile multi-layer replica chain across 7 databases and 900+ tables — delivering $320,000+ in annual infrastructure cost avoidance and reducing data latency from 48 hours to near real-time.

• • Designed and implemented a BigQuery data warehouse using dbt, supporting analytics and reporting across product, finance, and operations teams.

• • Built batch ingestion pipelines and analytics models; used Databricks and PySpark for large-scale transformations and performance-sensitive workloads.

• • Established team standards around testing, documentation, and code review; leads a data function of 8 direct reports including engineers, analysts, and interns, supporting both technical execution and career development.

• • Led migration of transactional data from a legacy platform to a new core system, maintaining data integrity across all analytical layers.

• • Designed and evolved an enterprise analytics warehouse supporting multiple business domains across product, operational, and financial data sources for a platform serving 125,000+ healthcare providers and 10 million patients.

• • Engineered a search metrics framework providing reliable, queryable insights into product search performance — a technically complex initiative requiring strong alignment between data architecture and business logic.

• • Built and maintained dbt models across staging, intermediate, and reporting layers across three GitLab repositories (analytics, mesh_core, mesh_product); all contributions peer-reviewed, approved, and deployed to production.

• • Developed ELT pipelines and introduced CI workflows for analytics transformations using version control and automated testing, improving deployment reliability.

• • Delivered 55+ merge requests across three repositories covering search metrics, dimension table modelling, RudderStack event staging, and data quality improvements.

• • Saved the business over $180,000 annually through pipeline automation, query optimisation, and warehouse re-architecture across operational and financial datasets.

• • Led migration from Amazon Redshift to Snowflake, improving scalability and query performance across analytical workloads.

• • Designed and operated batch and streaming data pipelines using Python, SQL, and PySpark for large operational and financial datasets.

• • Built Spark-based transformations and implemented dbt for warehouse transformations, adding data tests and documentation.

• • Collaborated closely with analytics and business teams to translate requirements into reliable, governed datasets.