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Datastage ETL
LinkedIn Recruiter Post
Aarorn Technologies seeks a Datastage ETL contractor with 5+ years of experience for a hybrid role in Toronto. Must have IBM InfoSphere DataStage, SQL, Unix scripting, and scheduling skills.
About the role
- Data Engineer responsible for designing and maintaining enterprise data warehouse for various projects. Collaborating with stakeholders to ensure efficient data flow and integration.
Responsibilities
- Design, develop, and maintain scalable Synapse pipelines for data ingestion, processing, and transformation in Azure data platform.
- Develop and optimize T-SQL and Python-based data processing scripts and integrate them with Synapse pipelines for ETL tasks and data transformations.
- Design and develop mapping dataflow solutions for complex data transformations.
- Develop and maintain data storage solutions, ensuring efficient data retrieval and storage on Azure Data Lake and databases.
- Implement and maintain API integrations, including REST APIs, for seamless data flow between systems.
- Design and test the end-to-end process for data ingestion, data transformation, data cleansing, data delivery, data quality and data timeliness.
- Implement and ensure data security and privacy measures, complying with industry standards and regulations.
- Monitor and optimize the performance of Azure data services, Azure Synapse pipelines, Function Apps, and Logic Apps.
- Improve the scalability, efficiency, and cost-effectiveness of data pipelines.
Requirements
- Bachelor's degree or equivalent in the field of Computer Science or Engineering
- 3+ years of Proven experience as a Data Engineer, specifically working with Microsoft Azure technologies.
- Proficient in T-SQL, Pyspark, Mapping Dataflow and Python programming language
- Hands-on experience with Azure data services, including Azure Data Factory, Azure Data Lake, Azure Function App, Azure Logic App, Azure SQL Database, Azure Synapse Analytics, mapping dataflow, and Event Hub.
- Understanding of data warehousing concepts and best practices.
- Experience with Data modeling.
- Experience in working with structured, semi-structured and unstructured data.
- Excellent written and verbal communication skills
- Excellent multi-tasking and organizational skills
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