Purpose
The Principal Specialist: Data & Analytics Engineering is the technical authority within the Data Engineering team, responsible for shaping how data is engineered, transformed, and delivered across the Merchant Division.
This role ensures:
HighâÃÂÃÂquality, scalable data architecture
Consistent engineering standards and best practices
Reliable, productionâÃÂÃÂgrade data pipelines
Alignment between engineering outputs and analytical needs
Continuous improvement of the data platform
You are the technical conscience of the team — ensuring that every solution is built correctly, consistently, and sustainably.
Key Responsibility Areas
Technical Leadership & Architecture
Own and define the endâÃÂÃÂtoâÃÂÃÂend data platform architecture.
Enforce engineering standards across pipelines, modelling, testing, CI/CD, and documentation.
Lead architectural reviews and guide complex technical decisions.
Identify and manage technical debt, scalability risks, and platform gaps.
Engineering Delivery
Lead delivery of complex, highâÃÂÃÂimpact data engineering workstreams.
Oversee multiâÃÂÃÂsource integrations and consolidation pipelines.
Own analytics engineering practices — including transformation and semantic layers.
Act as final technical reviewer for all productionâÃÂÃÂready artefacts.
Ensure alignment with Analytics & Insights for downstream consumption.
Mentorship & Capability Development
Provide handsâÃÂÃÂon technical mentorship to engineers.
Lead code reviews, knowledge sharing, and bestâÃÂÃÂpractice adoption.
Support hiring and onboarding of new technical talent.
Build and elevate team capability across data and analytics engineering.
Qualifications & Experience
Essential:
Bachelor's degree in Computer Science, Engineering, or related field
8+ years in data engineering and/or analytics engineering
Proven experience as a technical lead or principal engineer
Expertise in dbt or equivalent transformation tools
Strong experience with cloud data platforms (Snowflake, BigQuery, Databricks)
Advanced Python (pipelines, orchestration, testing)
Advanced SQL (optimisation, complex transformations)
Strong data modelling expertise (Kimball, Data Vault, OBT)
Ability to communicate complex technical concepts to stakeholders
Advantageous:
Financial services or fintech experience
MultiâÃÂÃÂentity or postâÃÂÃÂacquisition data environments
Streaming/eventâÃÂÃÂdriven architecture exposure
Data governance, lineage, and cataloguing tools
Closing Date 19 May 2026