Chris Mamon

Melbourne, Australia | +61 413 016 929 |  chrisam1993@live.com

github.com/Chr1sC0de |  chr1sc0de.github.io/Resume

Profile

Data scientist and backend-focused Python developer with experience building and operating production data platforms, ETL systems, CI/CD pipelines, and forecasting workflows. Strong background in platform reliability, deployment automation, and code quality, with research roots in computational fluid dynamics and extensive scientific programming experience.

Projects

• Built a Python, AWS, and Dagster proof of concept for ingesting, orchestrating, cleansing, and serving Australian energy market data through an event-driven ETL and lakehouse architecture.
• Designed and deployed 200+ assets across S3-backed Delta tables, PostgreSQL metadata, DynamoDB locking, ECS services, and Pulumi-managed infrastructure as code.
• Applied code quality automation, testing, and Codex AI-assisted implementation across ETL patterns, deployment setup, and platform documentation.

Employment

Teams: NEM Operational Forecasting

• Refactored and enhanced a Python-based Dagster ETL on Windows supporting around 50 assets for NEM operations, improving reliability across high-frequency backend workloads.
• Maintained R script ETL, Python scripts, and Podman-containerized jobs, with Dagster coordinating mixed execution patterns across operational forecasting workflows.
• Reduced failure rates in minute-level batch jobs by fixing networking and implementation issues, and migrated Dagster metadata from SQLite to Postgres to reduce cross-run impact.
• Standardized backend implementation patterns through factory-based refactors, improving consistency and making new asset and pipeline development easier to extend.
• Strengthened Azure DevOps CI/CD with Bash automation, Ruff, Prek, Pytest, and Mypy, improving code quality and deployment reliability.
• Built Pyinfra-based Dagster deployment, distributed Podman deployment, and multi-repository gRPC deployment, moving delivery from Windows Server toward self-hosted Linux CI/CD.
• Performed quarterly retraining and production deployment of Frequency Uncertainty Models, and fed BOM ensemble model data into ML forecasting pipelines.

Teams: Gas Market Real-Time Operations

• Reporting: Maintained a VBA reporting tool for weekly market insights and rotated analysis delivery.

• Process Automation

  • Automated extraction and validation of monthly AER and AEMO public data uploads using Python, saving about two hours per report.
  • Streamlined gas dataset QA with Python and Marimo notebooks, accelerating Quarterly Energy Dynamics validation.

• Operations Dashboards

  • Built dashboards for contingency gas operations, expiring trading rights, MOS stack checks, STTM and DWGM bids and offers, company contacts, and legacy NEOMobile report reconstruction.

• Operating System Lifecycle Testing

  • Tested application server upgrades from Microsoft SQL Server 2017 to 2022.
  • Automated stored procedure validation with Pytest to ensure UAT outputs matched production results.
  • Coordinated integration testing with cross-functional teams.

Teams: LPG Data Systems & Performance

• Documentation and Collaboration: Documented requirements in Confluence, collaborated through Asana, and worked in a GitOps-enabled Agile dashboard team.

• Stock Reconciliation

  • Automated Salesforce, SAP, and IVMS integration into Redshift with scheduled Matillion jobs.
  • Built a local Dagster-style pipeline connecting RoutePlanner, SharePoint, SGFleet, IVMS, SAP, Salesforce, and Outlook during an integration uplift.
  • Led requirements, data-model identification, ETL oversight, and dashboard delivery for LPG stock reconciliation.

• Asset Performance

  • Built end-to-end analytics dashboards using Salesforce and IVMS data to optimize truck scheduling and asset use, contributing to about $500,000 in cost savings.
  • Created reports that audited driver telemetry entries and exposed data-capture reliability issues for continuous improvement.

Teams: Trading Persistent and Trading Data & Systems

• Documentation and Collaboration: Documented business and technical requirements in Confluence and collaborated through Jira using Agile delivery practices.

• Plexos Data Extract

  • Developed a PySpark cloud ETL pipeline to extract Plexos forecasts into Databricks Delta tables.
  • Reduced trader analysis time by more than eight hours through pipeline optimization.
  • Orchestrated on-prem to cloud integration with Apache Airflow and Databricks ingestion tasks.
  • Added S3-triggered ingestion, New Relic logging and alerts, Terraform and Azure Pipelines automation, Python library deployment, and automated test, coverage, and SonarQube reporting.

• Hedging Strategy

  • Implemented hedging position logic in Databricks and built scheduled Airflow DAGs that coordinated workflows across accounts.
  • Automated Databricks Photon activation in Azure DevOps deployment pipelines.
• Cloud Data Program: Repeatedly executed automated business verification testing during a mission-critical lift-and-shift migration to preserve data accuracy and business continuity.
• AEMO Market Compensation Analysis: Calculated compensation required from market price caps and identified an amount of about $3 million.
• Caps Book Performance Evaluation: Supported dashboard query design comparing manual trades against automated strategy performance.
• CorvU Migration Support: Wrote SQL queries that helped migrate reports from external software into internal databases and Tableau services.

Teams: Blood Flow and Deep Learning

• Developed and documented scientific C++ and Python code for numerical modeling, visualization, and statistical analysis used in publications.
• Implemented computational fluid dynamics simulations on high-performance computing systems.
• Trialed convolutional neural networks for OCT image segmentation and shear stress prediction.
• Co-authored peer-reviewed journal papers listed under Publications.

Education

Masters of Engineering (With Distinction), Mechanical Specialization, The University of Melbourne Jan 2015 – Jul 2017

Melbourne |  Academic Transcript

Bachelor of Science, The University of Melbourne Jan 2012 – Dec 2014

Melbourne

Courses and Certificates

• Data Engineering, IBM on Coursera
• PostgreSQL for Everybody, University of Michigan on Coursera
• AWS Cloud Technical Essentials, AWS on Coursera
• Applied Data Science with Python, University of Michigan on Coursera
• Data Analytics, Google on Coursera
• Excel Skills for Data Analytics and Visualization, Macquarie University on Coursera

Publications

Link to Articles

• “Computational fluid dynamics comparisons of wall shear stress in patient-specific coronary artery bifurcation using coronary angiography and optical coherence tomography”, APS Division of Fluid Dynamics, August 2016
• “Effect of Medical Imaging Modalities on the simulated blood flow through a 3D reconstructed stented coronary artery segment”, 20th Australasian Fluid Mechanics Conference Perth, December 2016
• “Some new characteristics of the confined flow over circular cylinders at low Reynolds numbers”, International Journal of Heat and Fluid Flow, December 2020
• “High Spatial Endothelial Shear Stress Gradient Independently Predicts Site of Acute Coronary Plaque Rupture and Erosion”, Journal of Cardiovascular Research, July 2021
• “High Endothelial Shear Stress and Stress Gradient at Plaque Erosion Persist up to 12 Months”, International Journal of Cardiology, June 2022

Skills