Data Engineer - Audit Readiness

The team

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

Payward's Data team builds and operates the data infrastructure, pipelines, and analytics platforms that power decision-making, reporting, and controls across the business. The team partners closely with functions across the company, including Risk & Audit, IT, Finance, and Engineering, to make sure the data those functions depend on is reliable, well-governed, and ready to stand up to scrutiny.

This role sits within the Data & Audit Readiness team and is dedicated to supporting overall audit readiness from a data standpoint. You will own the inventories, playbooks, catalogs, and access processes that keep the Data team's systems and datasets audit ready, translating audit engagements directly into coordinated, well-documented responses. Because audit readiness is best designed rather than bolted on, this role calls for a data architect's background — someone who has built data pipelines before and knows how to shape them for traceability, access control, and testability from day one. This is a hands-on, builder's role where you will build the ETL pipelines, dashboards, and audit-ready data architecture that let Internal Audit turn audit and review planning into data-driven testing.

The opportunity

• Collaborate on the technical coordination and stewardship layer for shared datasets and systems in scope for audit, including financial systems, recordkeeping, and downstream reporting processes.
• Spearhead improvements to governance, communication, and coordination around upstream schema and logic changes that impact downstream systems and audit-related processes.
• Support technical analysis and downstream impact assessment during the change management process to improve downstream visibility for datasets, tables, and systems in scope for audit.
• Support the development and maintenance of reusable audit-ready dataset patterns, including automated validation, data quality checks, and monitoring.
• Review processes and datasets in scope for Audit, examine existing operational pain points, and propose reengineering opportunities and longer-term process improvements.
• Provide technical guidance to business stakeholders and the Audit PM around specific Audit requests.

What you bring

• 7+ years of experience in data analytics or data engineering, ideally supporting internal audit, risk, or SOX compliance functions within the fintech, payments, financial market or crypto where data quality and scale are non-negotiable.
• Experience managing and designing data architecture in a regulated environment, having designed and built data pipelines with governance, traceability, and audit requirements considered from the outset.
• Hands-on experience building and owning production data pipelines, with strong familiarity with dbt and Airflow or equivalent orchestration tools.
• Full mastery of SQL including complex joins, CTEs and analytical functions, alongside strong Python proficiency for pipeline development and analysis.
• Experience documenting technical and business requirements, including metadata, KPI definitions, business rules, data process flows, and data dictionaries.
• Familiarity with data administration tasks: job scheduling, error/exception handling, and troubleshooting production ETL workflows.
• Experience partnering with Data Engineering and/or Data Governance teams on shared datasets, schema changes, and downstream impact analysis.
• Strong communicator who can translate audit and business requirements into technical specifications and translate technical work back into terms non-technical stakeholders understand.

Nice to haves

• Experience building or scaling a data analytics function within an audit or risk organization from the ground up.
• Familiarity with AI-assisted or automation tools for data quality checks, testing, or workflow efficiency.
• Familiarity with audit management platforms (e.g., AuditBoard, Jira, Workiva).