ShyftLabs
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Business consulting · 51 to 200 Employees · Toronto, Canada
Hiring: Engineering
What employees say
+ Strong learning opportunities with modern technologies. Collaborative and supportive work environment. Exposure to global clients and enterprise projects. Good work-life balance (d
− Career growth opportunities may vary depending on the project. Internal processes can sometimes be slow. Workload and work-life balance depend on the client engagement. Limited ons
More reviews on the company profile.
Interview experience · AI/ML Engineer Intern
Based on Machine Learning and SQL, along with 2 linked list DSA questions in interview. But they were more interested to hire backend developers rather than ML engineers, but one of my friend got the offer for AI role du
Average difficulty · Noida · 8 reports
More interview insightsJob description
Overview
A Data Engineer role combining day-to-day pipeline maintenance with the design and optimisation of scalable data solutions, splitting time evenly between production support and new development.
Responsibilities
- Provide 50% of your time supporting existing data pipelines, monitoring, troubleshooting and resolving incidents
- Design, build and maintain the remaining 50% of scalable ETL/ELT workflows using Python, SQL and Spark
- Collaborate with cross-functional and client teams to translate data requirements into technical solutions
- Perform root-cause analysis on pipeline failures and implement fixes with minimal downtime
- Optimise data workflows for performance, reliability and cost-efficiency
- Document architecture, processes and support runbooks for knowledge continuity
- Participate in on-call rotations and support the client environment as needed
Qualifications
- 2–4 years of hands-on experience in a Data Engineering role
- Strong proficiency in Python and SQL
- Practical experience with Apache Spark
- Prior experience on client-based projects (mandatory)
- Ability to balance operational support with development tasks
- Excellent problem-solving and communication skills for client-facing situations
Nice to have
- Databricks experience
- AWS services such as S3, Glue, EMR, Lambda, Redshift
- CI/CD pipelines for data engineering
- Workflow orchestration tools like Airflow
Benefits
- Competitive salary
- Strong insurance package
- Extensive learning and development resources to support career growth
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