David De La Harpe Golden

Last Update: 2026-10-02

Python, Java and Linux programmer, devops and systems engineer. Extensive professional experience of financial-sector and other production-deployed backend and distributed data / big-data systems.

On finance technology team, delivering enhancements to existing portfolio accounting data ETL systems - bulk data loading and memory usage optimizations with significant wall-clock time savings. Resolution of multi-threaded correctness issues in a cache subsystem used in python ETL and API services. Automation of clustered portfolio accounting application upgrade processes in devops CI/CD pipeline. Enabling Kerberos auth for Kubernetes hosted services.

Key technologies: Python, Prefect, Falcon, PostgreSQL SQL RDBMS, SS&C Advent Geneva, Ansible, AWS Cloud, particularly EC2, EKS, RDS/Aurora, Kubernetes (K8s), Containers, Jenkins, Groovy, Git, Linux

On finance technology team, developing ETL processes for transfer of bitemporal financial data from a large (multi-TB) central portfolio accounting system to data warehouse for web API provision via micro-services and on-demand reporting to a strict day-1 production go-live deadline. Migration of deployment of ETL and microservice processes to containerized devops-style CI/CD pipeline on cloud-hosted Kubernetes cluster services. Metrics and logging observability pipeline development.

Key technologies: Python, Prefect, Falcon, FastAPI, PostgreSQL SQL RDBMS, SS&C Advent Geneva, AWS Cloud, particularly EC2, EKS, IAM, RDS/Aurora, Kubernetes (K8s), Containers, Datadog, Vector, Elasticsearch, Logstash, Jenkins, Groovy, Git, Linux

Enhancements to internal-facing global market risk data ETL, API provision, and reporting systems, built on an in-house bank python distributed data applications stack, presenting reports via React and OpenFin frontend.

Key technologies: Python, DB2 SQL RDBMS, distributed object database, Javascript, OpenFin, React

Sep 2018 - Sep 2019: Programmer (Contract), client: Bank of America Merrill Lynch

Enhancements to distributed and highly available FX post-trade system and supporting infrastructure, improvements to CI/CD processes, and internal-facing data reporting projects. Python data transforms and java (Spring) web service/app presentation.

Key technologies: Java, Spring Boot / Spring WebFlux, Swagger, Postman, Cloudera / Apache Big Data stack, Python, Airflow, SQLAlchemy, Flask, Jenkins, Ansible

Jan-Feb 2017, Apr-Oct 2017: Programmer (Contract), client: Houses of the Oireachtas

Adaptation for concurrent execution and parallelization of ETL processes. MongoDB to PostgreSQL migration. Directed acyclic graph based automated workflow design. Time-partitioned data integration of document database sources. Bulk load to Elasticsearch for presentation in public REST API. Architectural reviews. Parameterized clustered linux systems deployment. Mentoring of student junior developer on CRUD data maintenance web app summer project.

Key technologies: Airflow, Celery, Python, SQLAlchemy, Alembic, Ansible, Elasticsearch, PostgreSQL with btree_gist, Mongodb, Flask, RHEL, Linux

On a team developing automated configuration management / orchestration utility for linux-based HA clusters, a Python app that presented a RESTful web api for orchestration of puppet configurations to manage a Java-based telecoms network management solution, with parallelization of deployment and upgrade functions. Resolution of memory and concurrency issues including deadlock diagnosis and prevention in existing multi-threaded code.

Key technologies: Python, Cherrypy, Celery, SQLAlchemy, Alembic, PostgreSQL, Puppet, Ruby, Maven, Jenkins CI, Gdb, Valgrind Helgrind, RHEL, Linux

Financial trading systems development and systems integration. Server-side clustered async Java programming with vert.x, with secure frontend webapp with reactive js/html5 UI. Python glue code and devops tasks.

Key technologies: DB2, Java, RxJava, Vert.x (distributed actors), Hazelcast (in-memory data grid), WebSockets, jsse crypto/security, Datatables, Jython, CPython, JUnit, Ansible, Maven, QuickFIX, QuickFIX/J, Linux

Financial systems and network administration. High availability linux clustering. Systems and services virtualization. Second-site (Dublin/London) backup and disaster-recovery architecture and deployment. Development work on FIX protocol bridge to legacy financial software.

Development of a web interface for researchers using HPC systems, and misc. HPC systems administration and staff and end-user support as-needed.

Managed batch-processing linux clusters in DIAS (32 cores) and UCD (256 cores), used by researchers in astrophysics, geophysics and meteorology. The clusters were connected into the wider Irish (Grid-Ireland) and European (EGEE) research computing Grid.

On team offering services primarily within internal market of the ESB. Working on projects such as resolution of IT issues associated with business separation into state-regulated and unregulated units and a management information system for an eastern european environmental agency.

Java development for small startup working on web-based interaction with financial live data feeds

Developed a "secure extranet" java web application for controlled web-based access to mainframe-stored privileged information across organizational boundaries, for the assessment of social benefit claims.

Subjects studied included: Industrial management and economics, Impact engineering, Computational fluid dynamics and solid mechanics, Thermodynamics, Mathematics, Automatic control, Electrical engineering, Dimensional analysis, Information technology, Computing, Materials, Manufacturing technology (and more...)

Irish Leaving Certificate 1996: 5 A, 2 B, 1 C, higher level. / Irish Junior Certificate 1993: 6 A, 3 B, higher level.

Ongoing game development project work with Godot 4, Blender, Python. Longstanding Linux user since the 1990s.

Maintenance of personal website with photography gallery, with XWiki on Java 25 / Tomcat 10 on Linux, with TOTP authentication and DDoS/Bot protection with Anubis.

AI experiments and upskilling. familiarity with local LLM with open weights and preferably open source models via llama.cpp and openwebui. Agent confinement/sandboxing with traditional VM (libvirt/virt-manager). Python data / machine learning stack - pytorch, huggingface transformers and diffusers, etc. primarily on AMD gpu hardware with ROCm/HIP and Vulkan compute. Use of Dottxt Outlines for steerable generation.