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Data Engineer
2 months ago
We are seeking a highly skilled Data Engineer to join our team at Ontario Power Generation. As a Data Engineer, you will play a critical role in building and supporting data-driven applications that enable innovative, customer-centric digital experiences.
Key Responsibilities- Design and develop modular and scalable data ELT/ETL pipelines and data infrastructure leveraging a wide range of data sources across the organization.
- Implement curated common data models that offer an integrated, business-centric single source of truth for business intelligence, reporting, and downstream system use, in collaboration with Data Architects.
- Work closely with infrastructure and cyber teams to ensure data is secure in transit and at rest.
- Clean, prepare and optimize datasets for performance, ensuring lineage and quality controls are applied throughout the data integration cycle.
- Support Business Intelligence Analysts in modeling data for visualization and reporting, using dimensional data modeling and aggregation optimization methods.
- Troubleshoot issues related to ingestion, data transformation and pipeline performance, data accuracy, and integrity.
- Collaborate with business analysts, data scientists, data engineers, data analysts, solution architects, and data modelers to develop data pipelines to feed our data marketplace.
- Assist in identifying, designing, and implementing internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Work with tools in the Microsoft Stack; Azure Data Factory, Azure Data Lake, Azure SQL Databases, Azure Data Warehouse, Azure Synapse Analytics Services, Azure Databricks, Microsoft Purview, and Power BI.
- Work within the agile SCRUM work management framework in the delivery of products and services, including contributing to feature & user story backlog item development, and utilizing related Kanban/SCRUM toolsets.
- Assist in building data catalog and maintenance of relevant metadata for datasets published for enterprise use.
- Develop optimized, performant data pipelines and models at scale using technologies such as Python, Spark, and SQL, consuming data sources in XML, CSV, JSON, REST APIs, or other formats.
- Document as-built pipelines and data products within the product description, and utilize source control to ensure a maintainable code-base.
- Implement orchestration of data pipeline execution to ensure data products meet customer latency expectations, dependencies are managed, and datasets are as up-to-date as possible, with minimal disruption to end-customer use.
- Create tooling to help with day-to-day tasks, and reduce toil via automation wherever possible.
- Work with Continuous Integration/Continuous Delivery and DevOps pipelines to automate infrastructure, code delivery, and product enhancement isolation and proper release management and versioning.
- Monitor the ongoing operation of in-production solutions, assist in troubleshooting issues, and provide Tier 2 support for datasets produced by the team, on an as-required basis.
- Implement and manage appropriate access to data products via role-based access control.
- Write and perform automated unit and regression testing for data product builds, assist with user acceptance testing and system integration testing as required, and assist in the design of relevant test cases.
- Participate in peer code review sessions, and approve non-production pull requests.
- Other Duties as Required.
- 4-year University education in computer science, computer/software engineering or other relevant programs within data engineering, data analysis, artificial intelligence, or machine learning.
- Minimum 6 years experience with Data Engineering.
- Experience as a Data Engineer building data pipelines.
- Fluent in creating data processing frameworks using Python, PySpark, SparkSQL, and SQL.
- Experience with Azure Data Factory, ADLS, Synapse Analytics, and Databricks.
- Experience building data pipelines for Data Lakehouses and Data Warehouses.
- Good understanding of data structures and data processing frameworks.
- Knowledge of data governance and data quality principles.
- Effective communication skills to translate technical details to non-technical stakeholders.
