Team mentoring and coaching
A marketing analytics platform's CTO had built much of the application himself, but had never worked at the C level, and that inexperience became a real concern once the company started scaling with venture funding. Ollon provided six months of management coaching to help him grow into the role, working through what the CEO and investors actually needed to see beyond the technical decisions he was already comfortable making. The CTO ultimately chose to leave for an individual contributor role at another company, a decision the coaching helped him reach with clarity. That was a better outcome for the business than keeping him in a role that did not fit.
Hiring and onboarding support
A marketing analytics platform lost its CTO and senior developer within the same stretch of time, right in the middle of a critical scaling phase funded by new venture investment. Ollon led the search and hiring process for a seasoned replacement CTO and a new senior developer, then ran the onboarding needed to get both of them productive on a codebase neither of them had touched before. That hiring work had to move fast, since every week without permanent technical leadership put the company's scaling plans further at risk. The result was two experienced hires in place and functioning, not just recruited on paper.
Team augmentation and staff scaling
When a marketing analytics platform's CTO and senior developer left within the same window, the company had no one left who understood the codebase well enough to keep projects moving while permanent replacements were found. Ollon deployed an interim developer for nine months to fill that exact gap, keeping ongoing projects on track and holding the technical line until the new CTO and senior developer were hired and ready. That bridge role meant the company never had to pause active work or lose momentum on customer facing projects during the leadership transition. Once the new hires were fully ramped up, the interim role wound down and the augmented headcount returned to its normal level.
Knowledge transfer, training, and documentation
When a marketing analytics platform's CTO and senior developer left together, the technical knowledge of how the application actually worked was concentrated in exactly those two people, and neither one was staying to hand it off casually. Ollon's interim developer treated capturing that knowledge as the core of the role, documenting how the existing systems worked and then walking the incoming CTO and senior developer through it directly instead of leaving them to reverse engineer it on their own. That structured transfer meant the new hires ramped up on working knowledge instead of starting from nothing. The platform kept its institutional memory even though the two people who built it were both gone.
Monolithic to microservices migration
A marketing analytics platform's entire application ran as a single monolith on Elastic Beanstalk, a setup that made it hard to scale any one part of the system without redeploying all of it. Ollon migrated the deployment to ECS running Docker containers in stages, not as one single cutover, breaking the monolith into microservices designed around clear boundaries, including dedicated modules for data transformation. That staged approach kept the platform stable for existing customers, and the new boundaries meant a change or scaling need in one service no longer required touching the others. Each service's job ended up specific enough that new features could be added without risking the whole system.
ETL pipeline design and development
A marketing analytics platform needed to reliably capture customer journey data from client side scripts and get it processed and stored without losing events during traffic spikes. Ollon built a pipeline where client scripts sent data through API Gateway to trigger Lambda functions that sanitized the data and applied transformations like status and action tagging, then routed everything through SQS queues before final insertion into Redshift. The SQS layer specifically existed to absorb load spikes and guarantee delivery, so a burst of traffic did not mean lost or dropped events. That pipeline is what let the platform process customer journey data at scale without the fragility of a system built for a much smaller amount of traffic.
Infrastructure as code
A marketing analytics platform's infrastructure had grown without much formal structure behind it, making changes risky since no one could be fully certain what depended on what. Ollon rebuilt the infrastructure as Terraform code, documenting every backend change and new AWS resource as it moved off the platform's original deployment model. That documentation turned infrastructure changes into something that could be reviewed and reasoned about before they were applied, instead of discovered after something broke. The migration gave the platform infrastructure it could actually plan around, not just infrastructure that happened to be working at the moment.
Containerization
Each of a marketing analytics platform's newly separated microservices needed a consistent way to build, ship, and run, especially once there were multiple independent services instead of one deployable monolith. Ollon packaged each service into its own Docker container and ran them on ECS, giving every service the same deployment process regardless of what it actually did internally. That consistency meant a new service could be added to the platform using the same build and deploy pattern as every other one, instead of a bespoke setup each time. Containerizing the services also meant each one could be scaled up or down independently based on its own load, not the load on the system as a whole.
Observability and monitoring
After a marketing analytics platform's monolith was broken into several independent services, understanding how a single request actually moved through the system became much harder to see at a glance. Ollon set up monitoring across the new architecture using CloudWatch, AWS X-Ray, and Redshift Query Analytics, giving the team visibility into how requests traced across services and how the data pipeline was actually performing under real load. That visibility let the team catch performance problems in a specific service instead of only noticing that something, somewhere, had gotten slower. The distributed system stayed observable on an ongoing basis instead of only being understood after something already broke.
Large dataset handling
A marketing analytics platform needed its Redshift warehouse to reliably process over one million rows of customer journey data every day without query performance falling apart under that volume. Ollon configured Redshift's distribution and sort keys specifically for the platform's insert heavy workload, paired with Concurrency Scaling to handle simultaneous queries without slowing down ingestion, and minimized VACUUM operations since that insert heavy pattern meant the usual reasons for frequent vacuuming did not apply the same way here. Those choices let the warehouse keep up with both the daily data volume and the query load from teams actually using the analytics on top of it.
AWS
A marketing analytics platform's production footprint spanned Canada and the US, and each new environment brought its own traffic routing, credentials, and monitoring needs to sort out. Ollon tied those pieces together using load balancers to manage traffic across each environment, Vault to centralize secrets instead of scattering credentials service by service, and X-Ray with Redshift Query Analytics for visibility into how requests and queries performed. That foundation meant a new region or service did not mean re-solving how its traffic was managed, where its credentials lived, or how it would be monitored. The infrastructure could grow across regions without each new piece needing its own bespoke setup.
