Senior Data Engineer

Data pipelines don’t fail in staging. They fail at 3 a.m. on month-end, when compliance needs the numbers and the dashboard is showing yesterday’s data. You’ll build the pipelines that don’t break—and when they do, you’ll know before anyone else does.

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. You’ll own data accuracy end-to-end, raise the engineering bar for the team, and catch problems before they reach your stakeholders.

Why This Matters Deriv’s mission is Trading for Anyone, Anywhere, Anytime. Millions of traders, around the clock, across regulatory regimes. Every trade generates data. Every data point feeds analytics, compliance checks, fraud detection, and AI systems that serve customers in real time.

When a pipeline delivers stale data, a trader sees the wrong price. When a schema drifts undetected, a compliance report goes wrong. When governance is an afterthought, regulators ask questions nobody can answer. Data engineering at Deriv isn’t back-office plumbing. It’s the infrastructure that the entire business trusts.

Why Deriv We’re in production, not planning.

• Natural language interfaces querying the data warehouse directly—leaders ask questions in English, answers come back in seconds
• Continuous KPI monitoring with anomaly detection surfacing problems before stakeholders notice
• Dozens of fraud detection models running against production data, continuously
• 400+ users on our internal workflow orchestration platform, fed by the data infrastructure you’ll help build and scale

We share openly. Deriv⟨ed⟩ is where we write about what we’re shipping, what breaks, and what we figured out the hard way. You’ll join a transformation that’s underway, not one waiting for approval.

What You’ll Do Your work cuts across trading and product analytics, compliance and regulatory reporting, AI/ML feature pipelines, and business intelligence. Your placement depends on team needs and your strengths—your impact won’t be limited to one domain.

Build pipelines that are governed by design

• 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

Own data accuracy before anyone has to ask

• 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

Make the platform better than you found it

• Optimise warehouse performance and cost—query efficiency, partitioning, clustering, orchestration reliability
• 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

Raise the bar for the team

• 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

Who You Are You build pipelines, not just queries

• 6+ years in data engineering. You know that writing SQL is the easy part. The hard part is making sure data arrives on time, matches the schema you promised, and doesn’t silently break when upstre