Open to opportunities

Michael Adekola

@michaeladekola

Message

I build scalable cloud data platforms, streaming pipelines, and cost-efficient analytics systems.

Australia

Message

What I'm looking for

I'm open to contract and full-time opportunities where I can build scalable cloud data platforms, modernise legacy systems, improve data reliability, and deliver cost-efficient real-time analytics.

I've built and modernised cloud data platforms for FDJ Sportsbet, Sportsbet, Myer, KPMG, and public-sector clients, with a focus on reliable analytics, real-time processing, and practical cost control.

At FDJ Sportsbet, I led a Redshift-to-lakehouse migration and delivered the sportsbook division's first self-serve analytics platform. I resolved a Hive metastore defect that reduced refresh windows from hours to minutes and established Spark-on-Kubernetes standards supporting 10x data growth without added infrastructure spend.

At Sportsbet and Myer, I've improved Databricks and AWS Glue workloads through PySpark optimisation, streaming pipelines, data-quality controls, and monitoring. My work contributed to approximately 30% compute-cost reductions and lower pipeline latency.

I enjoy reverse-engineering legacy systems, designing event-driven architectures, and turning operational metadata into better engineering decisions. I've also built AI-assisted tooling for Databricks and SQL optimisation using LangChain, Spark logs, and workflow metadata.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

FDJ United

Feb 2026 - Present (7 months)

Led migration from Amazon Redshift to a medallion lakehouse with Kafka ingestion, dbt on Spark, and Airflow orchestration. Built dbt models with SCD1/SCD2 patterns and managed infrastructure with Terraform.

Kafka Java S3 Delta Lake DBT Spark Kyuubi Airflow Kubernetes Terraform SCD Type 1 SCD2 HiveMetastore AI Assisted Tooling

Data Engineer

Sportsbet

Feb 2025 - Jan 2026 (11 months)

Built and optimised real-time betting data pipelines on Databricks using Kafka, Spark Structured Streaming and PySpark. Reduced compute costs ~30% through Spark tuning, strengthened data quality with custom Great Expectations checks, modernised workloads to job clusters/serverless, and built AI-assisted tooling to identify Spark performance and DBU optimisation opportunities.

Databricks PySpark Kafka Spark Structured Streaming Great Expectations Grafana SQL Python ETL CI CD Amazon SQS GitHub Actions Unity Catalog Lang Chain

Data Engineer

Myer

Aug 2022 - Jan 2025 (2 years 5 months)

AWS data engineer responsible for AWS pipelines feeding e-commerce search and recommendation engine. Built event-driven pipeline with Step Functions, Lambda, DMS, and Glue, reducing latency by ~30% and Glue costs by ~30%.

AWS Step Functions Lambda DMS Glue DynamoDB EventBridge SNS Amazon SQS New Relic Jenkins Ansible Docker TypeScript PySpark

Data Engineer

KPMG

Jun 2021 - Jul 2022 (1 year 1 month)

Designed and implemented an end-to-end data platform for a university client using Azure Data Factory, Data Lake, Databricks, and Synapse, reducing query execution times by ~30%. Developed scalable ETL pipelines and an ETL pipeline for Yarra Valley Water on AWS.