Company Description Sifar Labs is a technology-focused organization dedicated to building data-driven products and solutions that help businesses make informed decisions. The team works with modern data platforms, cloud services, and analytics tools to create scalable and reliable data infrastructure. Sifar Labs values collaboration, continuous learning, and practical innovation in delivering meaningful business insights. Team members are encouraged to explore new technologies and approaches to improve data quality, performance, and usability.
Role Description This is a full-time, on-site Data Engineer role based in Toronto, ON. The Data Engineer will design, build, and maintain scalable data pipelines and infrastructure to support analytics and reporting needs. Daily responsibilities include implementing and optimizing ETL processes, developing data models, and integrating data from various internal and external sources. The role involves collaborating closely with data analysts, data scientists, and software engineers to ensure data is clean, reliable, and accessible. The Data Engineer will also monitor data systems for performance and reliability, troubleshoot issues, and contribute to documentation and best practices.
Qualifications
- Strong data engineering skills, including experience building and maintaining data pipelines and working with large datasets.
- Proficiency in data modeling and data warehousing, with the ability to design efficient schemas and storage structures.
- Hands-on experience with Extract Transform Load (ETL) processes and tools, including data integration and transformation workflows.
- Ability to perform data analytics, including querying, profiling, and interpreting data to support business and technical stakeholders.
- Experience with SQL and at least one programming language commonly used in data engineering (such as Python or Scala).
- Familiarity with cloud data platforms and services (e.g., AWS, Azure, or GCP) and modern data stack tools is beneficial.
- Understanding of data governance, data security, and best practices for data quality and reliability.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
- Ability to work collaboratively in an on-site environment, communicate clearly, and manage multiple priorities effectively.
