E
Emburse
Senior Engineering Manager (Analytics)
Skills
LookerData EngineeringSnowflakeData AnalysisAIAWSdbtApache AirflowFinancial PlanningPower BIAzure
3 days ago
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Important information
Contract type:
Permanent
Location:
Toronto, ON, Canada
Work mode:
Onsite
Published on:
18 October 2026
What they need
- As Senior Engineering Manager, Analytics, you will lead two data and analytics teams, Emburse Analytics and Travel Analytics, that turn travel and expense data into the dashboards, embedded reports, and AI-powered insights our customers use to control spend
- Reporting to the Director of Engineering in a hybrid role, you will focus on people and delivery: owning a strong SDLC, partnering with Product on options and tradeoffs, and ensuring good technical decision making
- Champions agile ways of working and continuously improves team rituals based on what the team learns
- Breaks large initiatives into small, incremental slices that ship value early and often, validated with customers before the next iteration
- Owns the team’s SDLC end to end, from refinement and estimation through testing, release, and post-release monitoring
- Partners closely with Product, presenting clear options with tradeoffs in scope, timeline, cost, and risk, and recommending a path forward
- Drives predictable delivery through realistic commitments and visible progress tracking
- Builds secure, compliant practices (SSDLC, GDPR, data residency, access controls) into the regular workflow
- Leads two analytics teams in delivering accurate, reliable data pipelines, models, and dashboards on budget and on time
- Establishes data quality standards so customers trust the numbers
- Improves engineering quality and efficiency through automation, test coverage, and cost management
- Hires qualified candidates to strengthen company and team
- Represents the analytics engineering teams across the organization and serves as the primary engineering partner to Analytics and Travel product owners
- Collaborate with cross-functional peers, vendor partners, and leaders to deliver projects
- Enough technical depth in data pipelines, data modeling, and BI to evaluate designs and assess risk and effort, while delegating implementation to senior engineers
- Accountable for the quality and security of customer-facing embedded analytics (Looker and Power BI) and the analytics roadmap, including a governed semantic layer for AI analytics
- Develop, lead, coach, and assess two teams of individual contributors across North America and Europe, growing senior engineers into technical leaders
- Grow a collaborative team culture grounded in trust, open feedback, and accountability
Benefits
- Flexible spending accounts
- Generous paid time off for every employee and flexible work schedules
- Paid leave for new parents
- Volunteering opportunities
- Health savings accounts (HSA)
- Medical, dental, vision, disability and life insurance plans
- Generous match to pre- or post-tax retirement savings accounts
- Financial planning services
- Program in partnership with eCornell to grow their knowledge and their careers
- Delicious supply of snacks and beverages
- Quarterly outings and Friday get-togethers to build a strong community at work- 8+ years of data engineering, analytics engineering, or software development experience, with 3+ years of direct line management, including leading multiple teams
- Required: Bachelor’s degree in Computer Science, Information Systems, or related field, or equivalent years’ experience
- Minimum of 3–5 years of direct line management experience, including leading multiple teams
- Proven track record of maturing SDLC and agile delivery practices across multiple teams
- Minimum of 8 years’ professional experience in data engineering, analytics engineering, or BI development
- Working familiarity with Snowflake, MS Fabric, dbt, Airflow, Looker, Power BI, and AWS or Azure
- Experience leading delivery of embedded analytics or AI analytics in a multi-tenant SaaS product
- Prior experience in the corporate travel, booking, or expense domain
- Ability to write clear, concise documentation
- Understanding of secure data practices and least-privilege access
- Strong understanding of dimensional modeling and common data design patterns
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