2020–2026
Nielsen Sports
Halifax, Nova Scotia, Canada
Director, Data Engineering
June 2024 – June 2026
Built and led a high-performing global engineering organization across the United States, Canada, Germany, Israel, and India, transforming manual operations into automated, AI-ready platforms. Accelerated AI adoption by championing an AI-first culture and empowering teams to innovate with clean, trusted data for advanced AI and ML solutions.
- Introduced an LLM-assisted code-generation workflow that increased engineering productivity by 35%, enabling delivery of one to three additional features per quarter.
- Managed a $1.25M engineering budget, redirecting 60% of investment from legacy systems to AI and ML.
- Engineered automated ETL pipelines that cut critical data-processing latency from two weeks to 45 minutes, enabling same-day advertising decisions for 80% of clients and real-time analytics for more than 1,000 business stakeholders.
- Established scalable data governance and compliance frameworks across a complex global data estate, strengthening data quality, trust, and regulatory alignment.
TechnologyDatabricks, AWS (Apache Iceberg, RDS, DynamoDB, Redshift, Lake Formation, EMR, DMS, MWAA, Glue, Lambda, Athena, S3, EC2), dbt, Spark, Python, MySQL, Elasticsearch, ClickHouse, Docker, Kubernetes, GitLab, Kiro, Amazon Q, LangChain (Deep Agents), Jira, Confluence, Miro
Senior Engineering Manager
June 2022 – June 2024
Architected and led a cloud-native data platform supporting an $85M sports analytics business. Directed the transition from monolithic architecture to event-driven microservices using Kafka and AWS DMS, delivering real-time insights to more than 80% of clients while establishing robust CI/CD and observability capabilities.
Partnered with Product teams to translate business priorities into architectural decisions, technical roadmaps, and sprint plans, balancing feature delivery with technical debt reduction through an 80/20 investment model.
- Designed and scaled a cloud-native data platform that reduced infrastructure costs by 40% through open-source adoption and architectural modernization.
- Replaced error-prone quarterly reporting processes with production-grade daily data pipelines, reducing data inconsistencies by 95% and strengthening stakeholder confidence in enterprise reporting.
- Established the organization’s first dedicated data engineering function, recruiting and scaling a team of more than 15 engineers and delivering a modern platform that reduced time-to-insight by 70% and became core infrastructure for product decision-making.
- Introduced disciplined platform planning and delivery practices that align architecture investments with product priorities, sustaining an 80/20 balance between feature delivery and technical debt reduction.
TechnologyAWS (RDS, DynamoDB, Redshift, DMS, Glue, Lambda, S3, EC2), Apache Kafka, dbt, Python, Java, MySQL, Elasticsearch, Docker, Kubernetes, GitLab, Jira, Confluence
Engineering Manager
January 2020 – June 2022
Led the modernization of legacy infrastructure into a high-throughput microservices architecture while establishing robust CI/CD and observability capabilities. Streamlined master data management for sports brand exposure products and harmonized multiple datasets to improve data consistency, accessibility, and operational efficiency.
- Led a nine-engineer team in rebuilding a legacy rates and ratings system, replacing a monolithic architecture with event-driven microservices using Kafka, Node.js, and AngularJS to increase data-processing throughput tenfold and reduce annual infrastructure costs by $100K.
- Implemented a CI/CD pipeline with automated testing, security scanning, and zero-downtime deployments, cutting deployment time from four hours to 20 minutes.
- Built a real-time infrastructure health monitoring platform that linked downtime to business impact, enabling proactive intervention and raising system availability to 99.95%.
- Delivered a 30% reduction in infrastructure costs through architectural optimization and operational efficiencies.
TechnologyAWS (RDS, DynamoDB, Lambda, S3, EC2), Apache Kafka, Java, AngularJS, MySQL, Elasticsearch, Docker, Kubernetes, HashiCorp Vault, Prometheus, Grafana, Datadog, GitHub, Jira, Confluence