Staff Data Engineer, Finance Data Platform

Who We Are

At the company, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. The company is a leading crypto exchange, and the developer of the company Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). The company is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. The company is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products the company, the company Wallet, OKLink and more.

About the team

The Finance Data Team is responsible for the data infrastructure that powers the company's compliance, regulatory, and financial reporting operations globally. Our Finance Data domain covers everything from raw transaction ingestion to consolidated group financials — supporting statutory reporting, board-level financials, and audit readiness across 100+ markets.

This is a team that treats data correctness as a non‑negotiable. We operate at the intersection of engineering rigor, financial domain knowledge, and regulatory accountability.

About the role

We are looking for a Staff Data Engineer to be the technical anchor for Finance Data at the company. 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.

Responsibilities:

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 the company'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 to improve financial data processes