Geoff Ennis Email & Phone Number

Melbourne, Victoria, Australia

Python/Cloud developer on the eReefs and MTU supercomputer upgrade projects. • Writing Data Pipelines in Airflow/MWAA/cylc to transform data from Bureau systems and run analytic models in ECS Fargate/cray supercomputer nodes. • Optimising python data analytics for hydrological, atmosperic dispersion and seasonal climate data. • Creating Docker images for python and Fortran analytics for use in Airflow/ECS. • Data format conversion using python, xarray, netCDF, and proprietary data formats. • DevOps pipeline building in GitHub/GitLab CI/CD for: Docker/ECR images, Airflow/MWAA DAGs, AWS Infrastructure in Cloudformation/Terraform, RPM build/deployment

Melbourne, Victoria, Australia

Lead developer for an algo trading startup, hosted in AWS, combining public and private data feeds for live trading and backtesting. • Designed/developed Python microservice MVP hosted in k8s, consuming from Redis, Kafka, FastAPI, websocket, APIs. Monitored using prometheus, Grafana • Maintaining EKS/EC2 instances, DevOps in GitHub/Jira, using Docker, Ansible, bash. • Historic data management in S3/python/spark/dask, to feed the MLOps machine • 24/7 system resiliency, support and stakeholder managment

Melbourne, Victoria, Australia

C# Quant/DevOps dev for Algo trading fund..Net Core service Docker containers running in AWS.DevOps for EC2 services using bitbucket, Jenkins, Cloudformation, Cloudwatch.RDS/MySql and reporting in Aws Lambda [python]

Melbourne, Victoria, Australia

Delivered performance system, Business Intelligence integration and portfolio management tools for the Private Markets portfolio. • Created server/cloud quant tasks to calculate portfolio performance [IRR, PME, DA], exposure, cashflow metrics in sql, python, databricks, spark, react. • Developed portfolio investment pacing model to predict future portfolio composition, given investment characteristics and performance assumptions, response to economic scenarios. • Power BI integration work, optimising data pipelining and modelling for dashboard performance and cloud integration • System monitoring in Elastic/Kibana. DevOps in bitbucket, github, bamboo

London, England, United Kingdom

Worked on extending the Contingent Liabilities Project within Credit Suisse’s multi-year Strategic Risk Program, to assess the impact of Credit Rating Downgrades on Credit Suisse. • Expanded the program to cover Securitised Finance Transactions, including scope definition, mapping trades to legal agreements, locating relevant legal docs, optimising workflow for legal resources. • Information Retrieval system [Apache Camel/MQ/Elastic/Apache Tika/tesseract] for processing documents to extract metadata and make text searchable. Trained machine learning classification models to identify relevant clauses in sklearn. • Quantitative data analysis of trade, sensitivity & collateral data in python/excel/sql.

London, England, United Kingdom

Working with the QEP Product team, producing reports for performance, positioning, investment analysis and review. • Creating interactive displays in Tableau to enable historic positioning & performance analysis. • Managing data pipelines from data providers into the QEP database, and from the QEP database to consumers. • Automating production of client PowerPoint presentations from template slides and standardised data queries

London, England, United Kingdom

Analyst developer working on the creation of a new business unit – Contingent Liabilities Management – for the assessment and management of credit rating downgrade risk. • Creation of prototype CL system in Oracle/Excel, to estimate the impact of collateral and unwind costs associated with a credit rating downgrade. • Improvements to a data capture system for Legal SMEs to capture details of relevant clauses in ISDA Master/CSAs, SPV docs and Confirms for relevant clauses and impacts. • Developed an internal querying tool in Angular/WCF for querying/presentation of legal, collateral, trade, risk and exposure data by IT, BAs, Legal SMEs • Ad-hoc data analysis in Oracle, C#, Python, Excel.

London, United Kingdom

Front Office developer focussed on improving trading processes and reducing operational risk for the Fund Management team within Quantitative Equity Products [QEP]. QEP manage 50+ funds with highly diverse portfolios and a variety of operational and legal restrictions. The trading team rely on a suite of tools to execute a high volume of trades without any operational and compliance issues. • Requirements Analysis on existing system and near term functional requirements, proposed and managed implementation plan to reduce reliance on VBA. • Planned/managed/developed migration of legacy Excel/SQL system to SOA WCF platform to extract business logic from display layer. • Developed and supported a suite of tools to improve operational efficiency: Cash level management, Leverage projection, CFD funding optimiser, Corporate Action management, counterparty risk management of portfolio swaps, FX forwards. • Integrated QEP Investment platform with Charles River IMS to remove manual process, reduce operational risk, and enrich trading data to enable execution analysis/mining. • Environment is C# .Net 4.0, with focus on WCF/Windows services and SQL2012

London

Front Office IT role in Structured Credit, Synthetic CDO and Structured Finance. • Maintained and improved daily PnL/Risk reporting system in Excel/VBA & C#/MSSQL • Requirements Analysis/Elicitation with Product Control, Risk Office, New Business for daily queries, implementing new structures and trade types • IT Skills used: Excel/VBA, C#, OO, .Net, SQL, WPF/WCF, Winforms, C++, COM, ActiveX, xll • Business Analysis, Sprint planning for junior team members. • Migrated the STG business from Excel/VBA to the strategic .Net ASPECT/TITAN risk platform on time as part of SDII, despite migration window reducing from 9 to 3 months. • Drove infrastructure improvements that allowed the rebooking of 2500 raw and incorrectly booked trades into golden source trade capture systems. • Supported and developed infrastructure for correlation trading desk, scaling the system from 200 trades to 3000+ trades whilst maintaining stability of risk and PnL. • Improved scalability and stability of Risk processes for exotic products, by more efficiently using CPU farm and task parallelisation • Maintained counterparty risk management sheets, including CVA pricers, EPE calculation, CSA netting.

Bachelor Of Engineering (Beng), Chemical Engineering, Honors

Bachelor Of Science (Bsc), Computer Science