~33m ago

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Senior Analytics Engineer - Finance

CanadaRemoteFull-timesenior

Data EngineerData

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Quick Summary

Requirements Summary

At least 7 years of experience in data analytics and data management within financial services or another regulated industry, including direct collaboration with Finance teams.

Technical Tools

Data EngineerData

Join a globally distributed data engineering team working at the intersection of financial data, accounting, and advanced analytics. As a Senior Analytics Engineer, you will build reliable financial data products that improve reporting accuracy, streamline accounting workflows, and support informed business decisions. You will transform complex financial information into scalable data pipelines, trusted models, automated reconciliations, and intuitive dashboards. Working closely with Finance, Accounting, Data Engineering, and Platform teams, you will help improve financial close processes and reduce manual operational work. You will also explore AI-assisted development and automation to accelerate investigations and deliver more efficient, dependable solutions. This remote opportunity offers significant technical ownership and the chance to strengthen financial data infrastructure in a dynamic digital asset environment.

Develop financial data products: Design, build, and maintain reliable data pipelines, transformation models, and analytics solutions that support financial reporting, accounting operations, and business decision-making.

Automate financial workflows: Partner with Finance teams to identify operational bottlenecks and implement automated processes for data preparation, reconciliations, reporting, and follow-ups, incorporating appropriate controls, monitoring, and exception handling.

Improve reporting and visualization: Create intuitive dashboards and data visualizations that help stakeholders monitor financial performance, investigate discrepancies, and make informed decisions.

Investigate and resolve data discrepancies: Collaborate with Finance, Product, and Engineering teams to diagnose reconciliation breaks, identify root causes, validate financial outputs, and communicate progress toward resolution.

Build scalable data pipelines: Collect, transform, and curate data from internal platforms and external systems, ensuring data accuracy, reliability, scalability, and accessibility.

Strengthen financial data infrastructure: Contribute to the development and implementation of a trusted finance data lake, establishing clear data definitions, ownership, quality controls, and validation processes.

Enhance data availability: Improve the timeliness of financial information and enable near-real-time insights where they deliver meaningful business value.

Leverage AI and emerging technologies: Evaluate new tools, prototype AI-assisted automation solutions, and integrate suitable technologies into production workflows to reduce repetitive work and accelerate development.

Support financial close activities: Improve recurring accounting and month-end close processes through sustainable automation, reliable data models, and transparent reconciliation workflows.

Ensure engineering excellence: Apply strong software engineering practices, including testing, documentation, version control, code reviews, and ongoing monitoring, to maintain production-quality data solutions.

Collaborate across technical and financial teams: Translate business needs into technical requirements, communicate complex findings clearly, align priorities with stakeholders, and encourage adoption of new data products.

Requirements

~2 min read

Professional experience: At least 7 years of experience in data analytics and data management within financial services or another regulated industry, including direct collaboration with Finance teams.

Programming and data analysis: Strong proficiency in SQL and Python, including the ability to develop, debug, test, and maintain production-grade code.

Data engineering expertise: Hands-on experience building and operating data pipelines and transformation models using dbt, Spark, Airflow, or comparable technologies.

Software engineering practices: Solid experience with Git, merge requests, code reviews, automated testing, and technical documentation.

Financial and accounting knowledge: Direct experience working with financial or accounting data, including reconciliations, subledgers, cost basis, or month-end close processes.

Reconciliation and data quality: Ability to investigate financial discrepancies, validate outputs with accounting stakeholders, identify root causes, and implement durable solutions.

End-to-end ownership: A proven track record of owning data pipelines throughout their lifecycle, making sound architectural decisions, and improving system reliability as business requirements evolve.

Automation mindset: Experience supporting recurring financial close activities and identifying opportunities to replace manual processes with scalable, maintainable automation.

Analytical and problem-solving skills: Strong judgment, attention to detail, and the ability to navigate ambiguous problems while remaining accountable for production outcomes.

Communication and collaboration: Excellent communication skills, with the ability to explain technical issues and their financial implications to both technical and non-technical stakeholders.

Autonomy and adaptability: Ability to prioritize complex work independently, collaborate across multidisciplinary teams, and adapt solutions to changing systems and business needs.

Nice to Have

~1 min read

Experience working with cryptocurrency transaction data and understanding how digital asset activity flows into financial records, accounting systems, and reconciliations.

Experience processing large volumes of financial transactions using distributed computing frameworks such as Spark or PySpark.

Familiarity with AI-assisted development tools and automation techniques for improving data engineering productivity.

What We Offer

~2 min read

Remote work: Work remotely from Canada as part of an internationally distributed team.

Technical ownership: Lead important financial data initiatives and influence the architecture and reliability of production data systems.

Cross-functional collaboration: Work closely with Finance, Accounting, Data Engineering, Product, and Platform teams.

Professional development: Expand your expertise in financial analytics, data engineering, accounting automation, and emerging AI-assisted technologies.

Meaningful impact: Improve financial reporting, data quality, operational efficiency, and decision-making across a global digital asset business.

Innovation-driven environment: Explore modern data technologies and automation approaches while solving complex financial data challenges.

Inclusive workplace: Join an organization that values diverse backgrounds, perspectives, and professional capabilities.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1

✓Remote work: Work remotely from Canada as part of an internationally distributed team.

✓Technical ownership: Lead important financial data initiatives and influence the architecture and reliability of production data systems.

✓Cross-functional collaboration: Work closely with Finance, Accounting, Data Engineering, Product, and Platform teams.

✓Professional development: Expand your expertise in financial analytics, data engineering, accounting automation, and emerging AI-assisted technologies.

✓Meaningful impact: Improve financial reporting, data quality, operational efficiency, and decision-making across a global digital asset business.

✓Innovation-driven environment: Explore modern data technologies and automation approaches while solving complex financial data challenges.

✓Inclusive workplace: Join an organization that values diverse backgrounds, perspectives, and professional capabilities.