Description
I continued my progression at Axis Group as a senior data engineer. My new role blended project leadership, client engagement and technical abilities in a hands-on-keyboard role. The defining feature of my time as a senior engineer was an expectation of increased client collaboration and strategic thinking. Leveraging an increased understanding of the data landscape I created sustainable solutions that made the lives of our clients easier. I partnered closely with executives and technical stakeholders to define solutions, translated business objectives into scalable architectures, and ensured successful implementation in high-stakes environments. I specialized in:
- Machine Learning modeling to continuously extract value from seemingly dormant data sources
- Architect data platforms that integrated with existing systems
- Best practices for onboarding of new tools
One of the most significant projects I led involved the inventory management system of a Fortune 500 company. My team was tasked with re-imagining how each location considers supply levels, discrepancies and pricing. I architected the data ingestion and analysis processes, partnered with executives to create a unified data strategy and implement the vision created. The project resulted in $20 million dollars of savings for the client and visibility into $67 million dollars worth of merchandise.
Beyond individual contributions, I piloted internal initiatives and knowledge to benefit the larger Axis organization. My role also required developing complex, business-critical solutions independently when timelines were tight or requirements were ambiguous, reinforcing my ability to deliver under pressure. Over the course of multiple engagements, I became a recognized subject matter expert in distributed data systems, Spark, and machine learning workflows—trusted by both clients and internal leadership for technical direction and strategic guidance.
Responsibilities
- Own and create data projects including but not limited to:
- Data Strategy
- Supervised and Unsupervised machine learning
- Data Science
- Machine Learning modeling
- Lead client engagements
- Own internal knowledge gathering and tooling initiatives
Projects
- Retail Fraud Detection
- Clustering Tuning
- Inventory Optimization
- Synapse Data Quality ETL
- MS Fabric Proof of Concept
Talks
Lessons Learned
- The most technically brilliant solution is not guaranteed to be the final solution