Senior Analytics Engineer, Finance

About the Company

Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.

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.

About the Role

We’re looking for a Senior Analytics Engineer to join Kraken’s Finance Data Eng & Analytics team within Data Platform. You’ll build trusted financial data products, using automation and AI to reduce manual work and improve the close process. Working at the intersection of data engineering and accounting, you’ll turn complex financial data into reliable pipelines, models and automated reconciliations. You’ll use AI-assisted tools to accelerate development and investigations, keeping financial outputs tested and traceable.

Responsibilities

• Work closely with Data Engineers, Finance and Platform Accounting to improve reporting and decision-making. Clear communication and collaboration will help you agree priorities, explain findings and drive adoption.
• Partner with Finance to understand workflows, identify pain points and translate business needs into practical data and automation solutions.
• Build and maintain automated workflows for data preparation, reconciliations, reporting and follow-ups, with clear controls, monitoring and exception handling.
• Build intuitive dashboards and visualizations that help stakeholders understand performance, investigate issues and make decisions.
• Resolve finance data issues and reconciliation breaks with Finance, Product and Engineering, addressing root causes and communicating progress.
• Build scalable, reliable pipelines to collect, transform and curate data from internal platforms and external systems.
• Support the design and rollout of a trusted finance data lake, with clear definitions, ownership and quality checks.
• Improve data timeliness, enabling near-real-time insights where they add business value.
• Evaluate new technologies, including AI-assisted automation, and prototype solutions to Finance needs that can be reliably supported in production.

Requirements

• 7+ years of experience in data analytics and data management within financial services or a regulated industry, working directly with Finance teams.
• Strong SQL and Python skills, including writing, debugging and maintaining production code.
• Solid experience with Git, merge requests, and code reviews, supported by strong software engineering practices, including testing and documentation.
• Hands-on experience building and operating pipelines and models using dbt, Spark, Airflow or comparable tools.
• Direct experience with finance or accounting data, such as reconciliations, subledgers, cost basis or month-end close, including investigating discrepancies and validating outputs with accounting stakeholders.
• A track record of owning pipelines end to end, making sound technical decisions and improving reliability as requirements and systems evolve.
• Judgment and autonomy to lead complex work, navigate ambiguity and remain accountable for production outcomes.
• Experience supporting recurring financial close activities, combined with a proactive approach to identifying manual processes and replacing them with reliable, sustainable automation.
• Excellent communication skills, with the ability to collaborate across Engineering and Finance, explain complex technical issues, articulate financial implications, and recommend actionable solutions.

Nice to Haves

• Experience with cryptocurrency transaction data and how digital-asset activity flows into financial records and reconciliations.
• Experience processing large volumes of financial transactions using distributed frameworks such as Spark or PySpark.