Tom Ogle
Senior Data Engineer Aws Data Platform & Lakehouse Architecture @Modular Data
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WORK HISTORY
Senior Data Engineer Aws Data Platform & Lakehouse Architecture @Modular Data
Senior data engineer operating at platform and architecture level within Data Mesh, Data Fabric, and domain-driven environments.Designed and delivered AWS-native lakehouse platforms, onboarding 100+ data sources via Avro-based data contracts to enforce controlled schema evolution across teams.Built and optimised large-scale CDC pipelines (Oracle & Postgres logical replication, WAL tuning) using Spark and Databricks\' open source Delta Lake distribution, implementing scalable MERGE/upsert strategies, version-aware processing, and domain-oriented serving layers in Redshift.Investigated Delta Lake transaction logs and file rewrite behaviour to improve clustering strategy, merge efficiency, and long-term performance characteristics of high-update CDC workloads.Delivered cloud-native platform infrastructure with Terraform (networking, compute, storage, streaming, observability) and implemented domain-driven serving layers in Redshift with integrated data quality controls.Python · Java · Kotlin · Spark · Flink · Delta Lake · dbt · Terraform · Kubernetes · AWS (DMS, Glue, Kinesis, Redshift, Athena, Lambda, CloudWatch) · Avro · Parquet · Deequ · Great Expectations
EDUCATION
The University of Sheffield
MComp (First-Class Hons) Masters and Bachelors
SKILLS
ABOUT TOM OGLE
I’m a Principal-level engineer specialising in distributed data platforms and ML-enabled systems. I design and build large-scale, cloud-native data architectures that power analytics, reporting, and machine learning workflows. My focus is on production-grade systems — reliable, scalable, and engineered with strong software discipline.Over the past decade, I’ve worked across enterprise and high-growth environments delivering:* High-volume distributed data pipelines (batch and streaming)* Data platforms supporting multi-terabyte and petabyte-scale workloads* Systems that feed and operationalise machine learning models* Batch training and large-scale inference pipelines* Productionisation of data science models from notebooks into robust services* Integration of model inference into streaming systems and APIsI work at the intersection of data engineering, distributed systems, and applied machine learning — bridging the gap between research/analytics environments and production infrastructure. My background includes hands-on software engineering across Python, Scala and Java; Spark-based processing; event-driven systems (Kafka, Flink); cloud infrastructure (AWS/GCP/Azure); and Infrastructure as Code. I apply strong engineering principles — CI/CD, testing, observability, system design and reliability — to both data platforms and ML-enabled workflows. I thrive in environments where data is mission-critical, where systems must scale, and where machine learning capabilities need to be operationalised responsibly and robustly.
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