Expertise
The areas I lead in, each backed by work described on the experience page.
Areas of practice
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Data platforms and pipelines
Lakehouse and warehouse platforms on Databricks and AWS, with automated ETL that took critical data-processing latency from two weeks to 45 minutes.
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Cloud modernization
Moving legacy estates to cloud-native architecture, from a 40-application middleware migration to a $15M modernization program that cut total cost of ownership by 40%.
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Distributed and event-driven systems
Replacing monoliths with Kafka-based microservices, including a rebuild that increased data-processing throughput tenfold.
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AI adoption in engineering
An LLM-assisted code-generation workflow that raised productivity by 35%, and a budget shift that moved 60% of investment from legacy systems to AI and ML.
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DevOps and reliability
CI/CD with automated testing and zero-downtime deployments, and monitoring that tied downtime to business impact and held availability at 99.95%.
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Data governance
Governance and compliance frameworks across a global data estate, and daily pipelines that reduced reporting inconsistencies by 95%.
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Building engineering organizations
Founding a data engineering function of more than 15 engineers, leading teams across five countries, and growing people into tech lead roles.
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Product and delivery partnership
Turning business priorities into roadmaps and sprint plans, with an 80/20 balance between feature delivery and technical debt reduction.
Technology
Technologies I have used over 24 years, from current cloud, data, and AI tooling to the enterprise platforms behind earlier work.
- Cloud (AWS)
- EC2, S3, EBS, ELB, IAM, AMI, EKS, Lambda, RDS, DynamoDB, Redshift, Athena, Glue, Lake Formation, EMR, DMS, MWAA
- Data platforms
- Databricks, Apache Spark, Apache Iceberg, dbt, ClickHouse
- Data engineering and streaming
- Apache Kafka, Debezium, ETL and ELT pipeline design, real-time data processing, data lake and warehouse architectures, event-driven systems
- Databases and search
- PostgreSQL, MySQL, Oracle, IBM DB2, Microsoft SQL Server, Microsoft Access, Elasticsearch
- AI and developer productivity
- Amazon Q, Kiro, LangChain, Deep Agents, LLM-assisted code generation
- Programming and backend
- Java, Python, .NET (C#, VB.NET, ASP.NET), VB6, Spring, Spring Boot, Struts, EJB, Hibernate, Node.js, RESTful and SOAP APIs, OpenAPI
- Frontend and UI
- AngularJS, React, Vue.js, JavaScript, SDL Tridion
- DevOps and infrastructure
- Docker, Kubernetes, Terraform, Vagrant, HashiCorp Vault, Maven, SonarQube, Sonatype Nexus
- CI/CD and observability
- Jenkins, GitHub Actions, GitLab CI, IBM UrbanCode, Prometheus, Grafana, Datadog
- Enterprise platforms
- IBM WebSphere, IBM MQ, IBM FileNet, IBM Case Manager, IBM Datacap, Oracle OTM, OBIEE, ODI, Actian DataConnect, Automation Anywhere (RPA), TeamSite, AIX, HP-UX, Windows Server
- Collaboration and delivery
- Git, GitHub, GitLab, SVN, Visual SourceSafe, Jira, Confluence, Miro, Scrum