Data Engineer — Payments & Funds-Flow

OSL; Kuala Lumpur; On-site; MYR 180,000 - 240,000; Full time

Job summary

OSL Group is seeking a data engineer to own the payments and funds-flow domain, aligning with payments, finance, compliance and risk teams. Core stack includes MaxCompute and DataWorks with CDC-based ingestion, and AI-native tooling to accelerate data work.

You will turn complex multi-source payment data into reliable models, ensure audit-ready accuracy, and own end-to-end data quality and SLAs across regions and currencies.

Qualifications

• 8+ years in data engineering or data warehousing.
• Strong SQL and dimensional modelling with end-to-end ownership of at least one subject area.
• Production CDC ingestion experience, including late and out-of-order events, deduplication, soft deletes and schema evolution.
• Accuracy engineering for financial data: idempotent writes, numeric precision, reconciliation and cross-system consistency checks.
• Agent-based development: multi-step agent workflows, tool and data-source integration through MCP, and reusable skills; context engineering, tool-call design, agent evaluation and debugging.
• Ownership of a data quality framework, covering rule design, threshold rationale, alert response and SLA commitments.

Skills

SQL

Dimensional modelling

CDC ingestion

Accuracy engineering

Agent-based development

Data quality framework

Tools

MaxCompute

DataWorks

MCP

LLM tooling

Job description

OSL Group is a listed digital-asset and fintech group; Banxa is our regulated crypto payments and fiat on/off-ramp business. This role is the data owner for the payments and funds-flow domain, serving the payments, finance, compliance and risk teams. Core stack is MaxCompute and DataWorks with CDC-based ingestion. The role works in an AI-native way by default.

What We're Really Looking For

We care more about how you think and the leverage you create than about any specific tool.

• Modelling judgement. You turn messy, multi-source payment flows into clean, reusable models, and can defend your choices on grain, dimensions and trade-offs. You've owned at least one subject area end to end.
• Correctness under compliance. You treat financial data as something that must reconcile and survive audit — idempotent, precise, verifiable — not just "loaded".
• Ownership over ambiguity. You engage upstream at product kick-off, pin down definitions before writing code, and stay accountable from ingestion to the business outcome rather than stopping at your layer.
• Influence without authority. You get backend, SRE, finance and compliance teams to move with you, and you set the quality bar and SLAs for a domain rather than following someone else's.
• AI-native leverage. You use AI agents/LLMs as a core part of how you build, review and operate data — automating the repetitive, accelerating the hard, and building data agents and copilots that raise the whole team's throughput. You have a point of view on where AI helps and where it must not be trusted.

Must-Have

• 8+ years in data engineering or data warehousing, including 2+ years in payments, settlement, financial services or exchange environments.
• Strong SQL and dimensional modelling, with end-to-end ownership of at least one subject area.
• Production CDC ingestion experience, including late and out-of-order events, deduplication, soft deletes and schema evolution.
• Accuracy engineering for financial data: idempotent writes, numeric precision, reconciliation and cross-system consistency checks.
• Agent-based development capability: multi-step agent workflows, tool and data-source integration through MCP, and reusable skills; context engineering, tool-call design, agent evaluation and debugging.
• Ownership of a data quality framework, covering rule design, threshold rationale, alert response and SLA commitments.

Preferred

• Multi-timezone, multi-currency and multi-region data, including business-day cut-off, FX treatment and localisation.
• Financial audit or regulatory-reporting support, including source-of-truth and explainable change history.
• Cross-engine migration experience, for example MaxCompute or Hadoop to Snowflake or Databricks.
• Building metadata, lineage, catalogue, metric platforms — or AI data agents / LLM-powered tooling.
• Python or Java for internal tooling and services.
• Familiarity with compliance constraints on architecture: PCI DSS, GDPR, data localisation, row and column level security, masking.

How We Work

• Definitional rigour — clarify before you code.
• Reproducible by default — idempotent, re-runnable, verifiable.
• End-to-end ownership — own the full chain and faces the business directly.
• AI-native — highest-leverage path first; automate the routine, keep human judgement where correctness and compliance demand it.