We are looking for a Staff Data Engineer to be the technical anchor for Finance Data at OKX. You will define how Finance data is architected, validated, and delivered, and set the standard for how the team operates in an AI-native way
You will work directly with Finance, Accounting, and Treasury stakeholders to ensure that financial data is complete, reconciled, traceable, and audit-ready. You will also be the domain's primary escalation point and the technical voice of Finance Data within the broader engineering organisation
This role suits someone who has operated as a de facto domain lead before — someone who has built financial data systems that have been reviewed by auditors, regulators and who knows what "good" looks like in a regulated financial data environment
Domain Architecture & Technical Direction
Own the end-to-end Finance data architecture: from ODS ingestion through CDM transformation to reporting mart, with clear data lineage documented at every layer
Define and enforce data modelling standards for Finance: period cut-off immutability, multi-entity consolidation logic, revenue recognition alignment, and audit trail completeness
Lead the design of scalable pipelines supporting trading data, asset positions, P&L, and cost accounting across multiple legal entities and jurisdictions (SG, EU, US, and others)
Make architectural decisions independently and articulate trade-offs clearly to both engineering peers and non-technical Finance stakeholders
Financial Reporting & Audit Readiness
Partner with Accounting, Finance PMO, and Treasury to deliver datasets for month-end close, statutory reporting, and board-level financials on time and to audit standard
Ensure every financial figure is traceable back to the source system with full transformation history — no black boxes
Build the data foundation that supports OKX's regulatory reporting obligations and long-term financial reporting maturity
Data Quality & Controls
Design proactive, self-validating DQC frameworks — reconciliation logic, SLA monitoring, and anomaly detection built into pipelines, not bolted on after incidents
Own incident response for Finance-critical pipeline failures; conduct structured post-mortems and drive permanent fixes
Define "done" for financial correctness in partnership with Accounting; hold the line on data quality standards even under delivery pressure
AI-Native Finance Data
Lead the team's adoption of AI-assisted engineering: LLM-assisted development, automated anomaly detection, intelligent reconciliation, AI-generated reporting summaries
Evaluate and introduce AI tooling into Finance data workflows with production evidence — not POC demos
Set the bar for what an AI-native Finance data team looks like, and bring junior engineers along
Technical Leadership & Influence
Be the go-to escalation point for Finance data domain questions across the team
Mentor and grow engineers working in the Finance domain; translate domain complexity into learnable patterns
Represent Finance Data in cross-team architecture discussions, sprint planning, and stakeholder reviews
Document domain knowledge that outlasts any individual — data dictionaries, pipeline specs, onboarding guides