This isn't a role where you maintain someone else's SQL. You'll build data infrastructure that powers trading decisions, product analytics, compliance, and AI/ML systems across a global fintech, own data accuracy end-to-end, raise the engineering bar for the team, and catch problems before they reach your stakeholders.
Design and build ETL/ELT pipelines across batch and real-time workloads using AI-assisted development, reducing build time without cutting reliability
Bake in observability from the start: freshness checks, completeness monitoring, schema drift detection, lineage tracking, and alerting. Not retrofitted after launch
Implement automated data quality checks and anomaly detection into every pipeline, governance built in, not bolted on
Identify and resolve data issues before they surface to analysts or stakeholders
Define and maintain data contracts: SLAs, SLOs, schema agreements, and producer-consumer alignment
Handle PII, access control, and auditability correctly in a regulated financial environment
Build data models designed to scale beyond the immediate use case: dimensional modelling, semantic layers, reusable abstractions
Spot gaps in the data platform and address them without waiting to be assigned
Partner with analysts, product, finance, and compliance teams to translate requirements into reliable, governed data products
Peer-review pipeline code and push quality standards higher across the team
Help onboard new engineers: share context, catch mistakes early, make others productive faster
Requirements
2-8 years in data engineering; you make sure data arrives on time, matches the schema you promised, and doesn't silently break when upstream changes
GCP, BigQuery, Airflow, Python, or the equivalent at comparable scale; you've built and maintained real pipelines across batch and streaming workloads
You've shipped with dbt or Dataform and understand why transformation layers matter
You use AI coding assistants daily as part of your workflow, not as a novelty
Data modelling techniques like Kimball star schema, Data Vault, or Medallion architecture; experience implementing data contract frameworks or schema registries at scale is a plus
You fix root causes, not symptoms, and explain what happened clearly to engineers and non-technical stakeholders
You share learnings actively, create reusable resources, unblock junior engineers and turn vague requirements into a spec
A fintech, trading, or compliance-heavy background is a strong plus, including handling PII under audit
Experience with warehouse cost optimisation and containerised data platforms
