Data Analytics Bi Engineer Jobs in Toronto, ON (NOW HIRING)

Role: Data Engineer
Location: Fully Remote (Canada, EST time zone)
Compensation: Salary + Bonus + Health Benefits

About Veem

Veem is transforming global money movement. Traditional cross-border payments are slow, expensive, and opaque—we’ve built a platform that makes them seamless, transparent, and scalable.

Our solution combines global payments, FX optimization, and embedded financial tools to help businesses—from SMBs to large platforms—operate and grow internationally with confidence.

We take a partner-first approach, working closely with customers to unlock revenue opportunities and drive real business impact.

Why Join Veem

• Impact: Help businesses move billions globally, more efficiently
• Growth: Be part of a fast-scaling fintech and embedded finance space
• Ownership: Contribute meaningfully and see results quickly
• Collaboration: Work cross-functionally across Product, Sales, and Ops
• Innovation: Shape the future of B2B payments

Job Description — Data Engineer, BI & Reporting

(Analytics Engineer / BI Engineer Hybrid)

About the Role

We’re hiring a Data Engineer, BI & Reporting to own and scale the reporting and analytics infrastructure that powers operational, revenue, customer, and executive decision-making.

This is a highly hands-on individual contributor role focused on:

• analytics engineering
• BI/reporting systems
• data modeling
• workflow automation
• AI-supported reporting operations

This is not a pure Data Analyst role and not a backend platform Data Engineer role.

The ideal candidate is an Analytics Engineer / BI Engineer hybrid who can:

• build clean SQL/dbt models
• structure scalable reporting datasets
• maintain dashboards and recurring reporting systems
• improve data quality and governance
• automate reporting workflows
• support AI-driven reporting and QA agents

You’ll partner closely with cross-functional stakeholders while owning the reliability, scalability, and governance of the reporting layer.

What You’ll DoAnalytics Engineering & Data Modeling

• Build and maintain scalable SQL/dbt data models, marts, semantic layers, and reporting datasets
• Clean, structure, and document complex or messy data systems
• Develop trusted reporting foundations for business teams
• Improve data consistency, metric governance, and reporting standards
• Design maintainable transformations and reusable analytics layers

BI & Reporting Ownership

• Own production dashboards, recurring reports, KPI packs, and reporting workflows
• Maintain and improve BI systems across business functions
• Partner with stakeholders to define KPIs, business logic, and reporting requirements
• Ensure dashboard accuracy, reliability, and usability
• Support self-serve analytics capabilities

Automation & AI-Supported Workflows

• Build or manage automated reporting workflows and monitoring systems
• Support AI agents and workflow automation related to:
  • reporting QA
  • data quality
  • KPI generation
  • dashboard monitoring
  • reporting automation
  • metric documentation
  • data freshness checks
• Review automated outputs and implement QA/governance processes
• Help transform manual reporting processes into scalable automated systems

Data Quality & Governance

• Implement data QA, validation, monitoring, and alerting
• Maintain data documentation, metric definitions, and reporting standards
• Improve observability and trust in reporting systems
• Troubleshoot reporting discrepancies and data issues proactively

RequirementsMust-Have Qualifications

• 3–6 years of experience in:
  • analytics engineering
  • BI engineering
  • reporting engineering
  • data analytics
  • data modeling
  • reporting automation
  • or similar fields
• Advanced SQL skills
• Strong hands-on dbt experience
• Experience building:
  • SQL tables
  • marts
  • semantic layers
  • reporting datasets
  • transformation pipelines
• Experience with BI tools such as:
  • Looker
  • Tableau
  • Power BI
  • Metabase
  • Sigma
  • Hex
  • Mode
  • or similar
• Experience maintaining dashboards and recurring reports in production environments
• Experience with data QA, monitoring, and reporting automation
• Strong documentation habits and QA mindset
• Ability to independently own reporting infrastructure and workflows

Bonus Qualifications

Strong bonus points for candidates with:

• Fintech, payments, or B2B SaaS experience
• Experience with:
  • HubSpot data
  • CRM data
  • revenue operations
  • customer success data
  • payments or transaction data
• KPI governance and metric definition experience
• Data freshness monitoring and alerting experience
• AI tooling or workflow automation experience involving:
  • OpenAI
  • Anthropic
  • n8n
  • AI agents
  • reporting bots
  • dashboard QA agents
  • workflow orchestration
• Experience automating manual reporting workflows

What Success Looks Like

• Reporting systems are reliable, scalable, and trusted
• Dashboards and KPI definitions remain consistent across teams
• Manual reporting work is significantly automated
• Data quality issues are proactively detected and resolved
• AI-supported reporting workflows operate with strong governance and QA

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