Advance Data Engineer
Role summary
We are seeking an Advanced Data Engineer in Canada (WFH) with strong experience in Python, PyTest, PySpark, Databricks, and Delta Live Tables. The role involves designing, building, and maintaining robust data pipelines for batch and streaming data, utilizing Azure cloud platforms, Databricks, and Delta Live Tables. Key responsibilities include data architecture and modeling, workflow orchestration, monitoring, and adhering to Agile principles. Experience with Unity Catalog is mandatory. Preferred qualifications include exposure to Github Actions, Datagog, data quality implementation, and CI/CD practices.
Description
This job posting is for an existing, active vacancy and We are looking to hire Advance Data Engineer in Canada (WFH), immediately who has strong experience in Python, PyTest, PySpark , Databricks and Delta Live Tables.
Role Overview:
Mandatory Skill:
- Programming Languages: Proficient in Python, PyTest and PySpark with a strong understanding software engineering best practices.
- Oops Concept: Strong Knowledge of It.
- Cloud Computing: Utilize Azure cloud-based data platforms, specifically leveraging Databricks and Delta Live Tables for data engineering tasks, while effectively utilizing services related to storage, compute, and security.
- Data Pipelines: Design, build, and maintain robust and scalable and automated data pipelines for batch and streaming data ingestion of data and processing (Data bricks workflow).
- Data Architecture and Modeling: Design and implement robust data models and architectures that align with business requirements and support efficient data processing, analysis, and reporting.
- Orchestration: Utilize workflow orchestration tools to automate data pipeline execution and dependency management.
- Monitoring and Alerting: Integrate monitoring and alerting mechanisms to track pipeline health, identify performance bottlenecks, and proactively address issues.
- Strong Agile principles: Utilize Agile development methodologies, actively participating in sprint planning, daily stand-ups, sprint reviews, and retrospectives. Be flexible and adaptable to changing requirements and priorities throughout the project lifecycle.
- Unity Catalog
Good to Have:
- Github Actiom
- Datagog exposure
- Data Quality: Implement data quality checks and balances throughout the data pipeline, including profiling, validation, and root cause analysis, to ensure data accuracy, completeness, and consistency.
- CI/CD: Implement continuous integration and continuous delivery (CI/CD) practices for automated testing and deployment of data pipelines.