Senior Data Engineer
ShyftLabs · Hybrid
MeritLog read this listing from ShyftLabs's Lever job board and last checked it on September 12, 2026.
Source: the employer's Lever job board. Open the original listing for current details.
Job details
- Work model
- Hybrid
- Salary
- Not listed by source
- Location
- Toronto, Ontario
- Occupation
- Database Architects(O*NET 15-1243.00)
Hiring context
How this role compares at ShyftLabs
ShyftLabs has 26 live roles in MeritLog’s catalog across 5 job families, and 11 of them are in data & analytics. 3 of those listings publish a pay range, a disclosure rate of 12%.
ShyftLabs concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
Job description
About ShyftLabs At ShyftLabs, we live and breathe data. Since 2020, we've been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We're growing fast, and we're looking for passionate technical leaders who are excited to solve complex data challenges, build modern cloud platforms, and deliver innovative solutions for enterprise clients. The Opportunity ShyftLabs is seeking an experienced Senior / Lead Data Engineer to lead the design, architecture, and delivery of enterprise-scale data platforms for Fortune 500 organizations. This is a highly client-facing leadership role responsible for owning projects from discovery through production deployment. You'll partner directly with client stakeholders to understand business objectives, define technical strategy, architect scalable cloud solutions, and lead engineering teams through successful delivery. The ideal candidate combines deep hands-on expertise with Databricks, Apache Spark, Python, SQL, and modern cloud platforms with proven experience leading complex data modernization initiatives. You'll play a key role in shaping technical direction, mentoring engineers, establishing engineering best practices, and delivering scalable data products that enable analytics, AI, and machine learning.