Curriculum Vitae

Brett Ryan

Chief Data & Solutions Architect | Real-Time Decisioning

me@brettryan.dev LinkedIn | Stack Overflow | GitHub

PROFESSIONAL SUMMARY

During my over two decade career as a solutions specialist, I’ve worked on projects from small memory constrained devices to large, massively distributed data platforms where I’ve built out operational workloads that need to respond to real-time data problems. In my recent time at Telstra, I’ve specialised in designing and building a customer recommendation engine, decisioning platform and a Customer Analytics Record (CAR) solution that generates aggregates in real-time.

As a software engineer by heart, I specialise in the Java ecosystem where I participate with live discussions on Java events, and was a prior member of the NetBeans IDE contributor team.

My recent focus has been on building out designs that have a heavy focus on data specialisation through efficient near real-time data processing pipelines that are reactive in nature.

SPEAKING & OPEN SOURCE CONTRIBUTION

During my time I’ve been a big supporter of OpenSource projects where I was a supporter and test lead for the NetBeans IDE Community Acceptance Test (CAT) team for several years. I have published several projects to my Github account and have been a community support member on the Stack Overflow and Stack Exchange platforms which you can find by visiting my profiles at the top of this document.

MENTORSHIP & INTERNAL LEADERSHIP

I am a strong believer in educating our younger people and am passionate about this. I currently have 2 official mentees in the mentor program who have reached out to me asking for me to be their mentor, while also providing mentoring capacity to those that reach out. Watching someone else shine through guidance is incredibly rewarding.

Contributing to events such as Telstra Cloud Days, and Data & AI Academy as MC and speaker have been programs that I’m always willing to put my hand up for. Brown bag sessions and live code demonstrations also gives me reward.

SKILL SUMMARY

Languages

Java, Scala, ECMAScript (JavaScript), C#, Postgres, HTML, CSS, Bash, KSH, Functional Programming

Platforms

Hadoop, HDFS, Hive, Spark, Kafka, AWS Ecosystem (Step Functions, Lambda, Batch, DynamoDB, Postgres, MySQL

Tooling

Git, JIRA, Confluence, Bamboo, Bitbucket, Linux, Docker, Terraform

TECHNICAL SKILLS

Languages

Through my career I’ve worked with many languages and companion languages within projects ranging from markup, LISP style languages through to higher order functional languages such as Scala. I’m highly engaged in the Java ecosystem and consider it to be an area I have a high degree of competence. I have notable areas of expertise with Scala, ECMAScript (JavaScript) and C# among others.

Data Platforms

Starting with the Progress OpenEdge platform I specialised in ABL App Servers. I have for the past decade been specialising in distributed data platforms that are rooted in the Hadoop HDFS for OLAP data warehousing such as Apache Hive, Hadoop, Amazon Redshift, Athena and the Glue catalog. Orchestration tools with Apache Airflow and Apache NiFi for activation layers. I have a solid demonstrated ability for building highly efficient single-table designed NoSQL designs being delivered by the Amazon DynamoDB capabilities with reactive programming against the Kinesis Data Streams that it emits.

Architecture

Designing and building out event-driven solutions that focus on separation of concerns with Domain Driven design that is composed of domain focussed reusable components, often incorporating a micro-services design.

DevOps & Tools

Being an early adopter of DevOps, I have always led the way to Agile maturity. I was a member of the Atlassian codergeist and Atlassian Dragon slayers program. I’ve developed plugins for Confluence and JIRA to enrich these capabilities.

I was also an early adopter for Git after seeing Linus’ Google talk, while; originally starting my career with RCS and CVS.

I’ve used various DevOps tools such as CodeCommit, Gitlab, Github, Bamboo, Bitbucket, and also manage my own SSH based Git backend server.

Using Terraform to do all cloud infrastructure automation, I am more comfortable with terraform than finding things in the AWS console and am against click-ops.

RECOGNITION & AWARDS (Telstra, Melbourne)

Opolo Replacement

With the confluence of the Customer Analytics Record, Real-Time Decisioning and a new Journey Builder, we have eliminated the Opolo Pre-Paid solution which not only saved () pa in operational expenses; it has enabled the activation of strategic ML driven marketing decisions that forecast a retention and growth revenue of $14 million in the FY26 calendar. This resulted in my team being awarded a quarterly award of excellence.

Medallia Experience Orchestrator Replacement

Saving $1 million a year in operational costs, we replaced the MXO capability with a purpose built solution that is closer to our recommendation engine. This not only reduced the OPEX cost, it enabled us to surface recommendations in real-time and get real-time feedback. For this effort my team and I were awarded a quarterly award of excellence.

Dynamic Actions

Enhancing the Next Best Action (NBA) capability, my team and I delivered machinery that allows for actions to be dynamically assessed at runtime as they are requested. This enabled channels to have a richer experience with the recommendation engine that allowed for channels to enrich point in time messages to customers, provide suppression and saturation rules. This resulted in the members involved in delivering this effort a quarterly award of excellence.

Fast Path

Starting off as a Proof of Concept (PoC) which led to the Customer Analytics Record (CAR) becoming its successor; Fast-Path enabled real-time detection and alerting integration to the decision engine that handled vulnerability, outage, and other customer care activities. My team and I were recognised for delivering a solution that put our customers first and were awarded a quarterly award of excellence.

PROFESSIONAL EXPERIENCE

While in the Telstra/Accenture Joint Venture, I have been defining the role of decision intelligence architectural components to deliver an ML enabled solution that is closer to business units by breaking down technical barriers to activate decisioning solutions. This has involved the negotiation and design of the following components.

• Centralised rules repository and execution platform
• Centralised decisioning platform and engine
• Eventing pipeline for reactive decisions
• State management facility for tracking decision behaviour over time
• Champion/Challenger and A/B testing support for incremental and whole change evaluation.
• Simulation engine that guides data preparation of a decision simulation

Focussing on a real-time recommendation engine, I designed and oversaw the build of a Customer Analytical Record (CAR) facility that incorporated a reactive feature engine that allows feature authors to describe feature definitions that are executed reactively to data change. There were multiple capabilities that were engineered in this time.

• Single table DynamoDB backed Customer Analytical Record
• Real-time feature engine that allows describing dependent entities to reactively aggregate new features
• Horizontally scalable currently serving 35k feature evaluations per second.
• Event bus messaging pipeline to alert relevant systems when feature states have changed to enable real-time activation or scoring
• Real time recommendation and decisioning platform to react to feature events and inject customers into real-time decision flows.
• Delayed message queue that allows for the queueing of messages to be emitted to consumers at a specific point in time.

Starting with a Spark Scala, Hadoop HDFS backed solution, I designed and built a recommendation engine that allows for user described rules through a drag and drop user interface that allows for the targeting of customers. Providing a targeted recommendation engine, users can describe marketing campaigns that manage communications and offer bestowing when customers have been monitored for desired outcomes.

• Recommendation engine processing up to 1.5 billion decisions a day across over 1000 individual recommendations.
• Decision engine that resolves recommendations for both outbound and inbound communication channels.
• Next Best Action (NBA) API’s for channel interaction with the recommendation engine.

John Sands had made the strategic decision to move its operational activities to the sister company UK Greetings, which operated on different technology than that used at John Sands. During this time I led the software migration from the John Sands environment to the UKG environment. While there were several significant notable challenges to achieve this, it was ultimately successful and led to my redundancy in 2017.

During my time as a software engineer at John Sands, I was fortunate enough to work on an amazing array of technologies and systems that were all bound, resulting in some very challenging capabilities being developed.

• Display Management System (DMS): C# WPF/CAD solution for retail design with a Java/Progress OpenEdge backend.
• Right Project System (RPS): JavaEE/Spring system managing the lifecycle of 10,000+ SKUs annually.
• Field Sales Automator (FSA): Mobile application managing 500+ merchandisers across ANZ.
• Voice Mail Automation Studio: Desktop PABX integration and call routing tool.

Notable for starting my career as a software engineer, I started as a retail space planner where I was responsible for designing the layouts of retail store products within the greeting card and related products space. The significance of this role is it drove me to create software to better do my job, which led me to be hired by the applications development team to pursue these developments that evolved into writing all the software for marketing, manufacturing and retail spaces within the company.