I design and organise the pipelines that turn raw, messy data into something a business can actually trust — and I translate what it's telling us into plain language.
I am a Senior Data Engineer with a deep interest in technology and software development, and a lasting curiosity about how computing systems work under the hood.
I am particularly passionate about building custom data platforms on open-source technologies, designed from the ground up to be secure, compliant and tailored to the needs of the business.
I am solutions-oriented and do my best work on complex problems that have a direct, measurable impact on product and business outcomes.
I design, build and maintain reliable data platforms and pipelines, ensuring that data is well-governed, high-quality and readily available. I monitor for trends and inconsistencies that may affect business objectives, and I communicate these insights clearly to stakeholders so the organisation can make informed, data-driven decisions.
Working within Agile teams, I deliver customer-focused solutions iteratively, helping the team maintain focus on rapid, high-quality delivery.
Building LLM agents and retrieval-augmented generation (RAG) solutions on top of the data platforms I engineer.
A cloud-agnostic data platform that brings polyglot notebooks, pipeline orchestration, an Iceberg-native open warehouse and a unified catalog with column-level lineage under one control plane, running across AWS, GCP and Azure with data kept in the customer's own VPC.
Visit sowndata.comA data-cleaning demo built on Polars — a lightning-fast DataFrame library with a multi-threaded, Rust-powered query engine.
View projectSharpening problem-solving and algorithmic thinking through regular kata practice.
View profilePractising data structures, SQL and core engineering challenges across domains.
View profileLed migration of close-book systems data to the enterprise data platform; managed delivery and team coordination
OutcomeSuccessfully migrated Flexi and Omega into the enterprise data model; reduced mainframe footprint, lowered operating costs and vantage MIPS usage
Data engineer responsible for platform build, data source integration, and transitioning portfolio models
OutcomeInitiated Databricks implementation as the unified platform for governance, AI and analytics; began portfolio model transition from legacy warehouse to Spark/Databricks
Supported modernisation program; adopted new standards and migrated data to modern lakehouse architecture
OutcomeCompleted data lineage integration into the enterprise data catalogue; automated key processes eliminating manual intervention in legacy workflows
Led and executed quarterly data source integration planning and delivery for the enterprise data platform
OutcomeIntegrated 11 data sources into the platform, exceeding the annual target of 10; strengthened platform reach, performance and data quality
Data engineer responsible for productionalising AML data feeds and supporting SAM data requirements
OutcomeProductionalised remaining AML feeds; progressed SAM data requirements to support regulatory compliance obligations
Contributed to designing and implementing a data quality monitoring framework across the group
OutcomeCreated structures to monitor data quality for PAS data; enabled remediation at source across multiple milestone deliveries
Data engineer contributing to DHA data product development and Customer360 profile expansion
OutcomeDelivered DHA data product MVP; expanded Customer360 profiles; advanced omnichannel design and assessed client ID/onboarding strategy
Implemented real-time metadata updates, AI/ML metadata enrichment and data lineage visualisation in collaboration with strategic vendors
OutcomeEnabled real-time metadata sync across integrated systems; improved data discoverability and transparency through enhanced lineage visualisation
Led migration of CI/CD pipelines and embedded DevSecOps practices across the engineering team
OutcomeMigrated all pipelines to Azure; enforced linting and peer review; achieved 20% increase in security adoption; maintained 2-year incident-free record with zero unauthorised production changes
Collaborated with Cloud/DevOps team to audit, identify and implement tooling and cloud cost-saving initiatives
OutcomeAchieved annual cost savings target; reduced wastage through tooling consolidation, contract renegotiations and right-sizing of cloud resources
Cross-skilled team members on Confluent Kafka streaming; managed knowledge transfer and delivery
OutcomeSuccessfully cross-skilled team resources on streaming concepts; improved team capability and resilience across streaming workloads
Supported decommissioning of legacy data warehouse systems
OutcomeContributed to the decommission program, reducing estate complexity and stranded costs while supporting migration to modern target architecture
Open to data engineering work, collaborations and good conversations about pipelines. Drop me a line.