Senior Data Engineer
| Company | ResQ |
|---|---|
| Location | Toronto, ON, Canada, Miami, FL, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior |
Requirements
- Bachelor’s degree in Computer Science, or a related field, or equivalent experience.
- 5+ years of experience in data engineering or a related role within a tech environment.
- Proficiency in data technologies such as SQL and DBT, and in programming in Python.
- Proficiency in SQL, Python, and cloud-based data warehousing tools; familiarity with DBT, Airflow, and BigQuery (or similar).
- Proven experience in working with cross-functional teams and presenting data-driven recommendations to senior stakeholders.
- Strong understanding of product development processes.
- Exceptional problem-solving skills, attention to detail, and ability to thrive in a fast-paced, dynamic environment.
Responsibilities
- Build and Maintain Data Pipelines: Design, develop, and optimize ETL pipelines using tools like Airflow or similar workflow orchestration systems. Ensure data reliability and efficiency for downstream analytics and reporting needs.
- Data Modeling and Transformation: Implement and maintain data models in DBT (or similar), ensuring data consistency, quality, and readiness for analysis. Develop and manage transformation workflows to support scalable data warehousing.
- Data Warehousing and Storage: Work within cloud data warehouses (such as Google BigQuery) to ensure optimized data storage and retrieval. Develop best practices for data warehousing, focusing on cost efficiency and performance.
- Collaboration and Support: Partner with data analysts, product teams, and stakeholders to understand data requirements and translate them into technical solutions. Provide guidance and support for data-driven projects, ensuring the right data is available for business insights.
- Monitoring and Optimization: Implement monitoring tools to ensure data quality and pipeline health, proactively troubleshooting issues. Continuously optimize data processes for efficiency, scalability, and cost-effectiveness.
- Documentation and Best Practices: Document data processes, models, and workflows to ensure clarity and transfer of knowledge within the team. Promote best practices in data engineering and data governance.
Preferred Qualifications
- Knowledge of machine learning and inference, including data mining.
- Experience with dashboarding tools such as Looker (or other BI tool)