Experience

Led the multi-year data modernisation for the Attractions and Rides businesses, supporting £200M+ revenue: 15+ cross-functional teams, 70% efficiency improvement, zero business disruption.

Completed the Hadoop to Snowflake migration on its April 2026 target: 209 workflows and 342 data assets across 12 legacy systems. Redesigned data contracts for future data products and left the business's data fit for AI.

Secured alignment across Legal, Security, and Risk by facilitating workshops with 50+ stakeholders.

Defined and executed the end-to-end data architecture that launched the Rails train-ticket vertical, a new consumer market under the Connected Trip strategy.

Productionised an ML deduplication algorithm that removed 10,000+ duplicate attractions and generated 8% additional revenue.

Built an agentic knowledge platform answering natural-language questions across 200+ data assets by routing each question to the relevant encoded business-domain skill, then applying that skill's logic before running the query. Also automated the Jira to execution to GitLab pipeline and AI-driven project reporting.

Designed and launched the Partner Intelligence Platform, a self-service data portal for third-party providers: 50 partners activated in the first 3 months, roughly 2 weeks per quarter of ad-hoc analyst work eliminated, DMA compliance delivered on schedule.

Directly led 6 engineers and 1 PM; acting people leader during Engineering Manager absence.

Drove a 12% engagement improvement through a user-centred redesign of the offers and benefits section at a regulated European neobank.

Led product discovery and cross-functional collaboration across design and engineering.

Led a team of 11 engineers (mobile, full stack, data) to redesign the data architecture and recommendation system: 7% conversion improvement and 10% engagement improvement at 1M+ monthly active users.

Developed an organisation-wide behavioural data strategy adopted across marketing, commercial, and sales departments.

Ran company-wide training sessions reaching 100+ team members.

Built scalable payment flows and user-management microservices on Kubernetes for a fintech, alongside real-time streaming architecture for executive dashboards.

Built real-time pipelines with Kafka, Flink, Spark, Couchbase, and Cassandra at Alibaba-owned Trendyol.

Developed ML workflows with Spark MLlib for product similarity, including a deep learning similarity model and user-preferred attribute mining per category, working closely with marketing and operations.

Worked across Spark, Kinesis, AWS EMR, Scala, Elasticsearch, S3, Redis, and Athena.

Rewrote an ML user-interest clustering application with Spark MLlib and built cart recommendation, trending product, and similar-product algorithms.

Started as a data science intern and became a part-time data engineer after 5 months; analysed click-event data for a national newspaper, consuming from Kinesis into S3.