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Lead Data Engineer IRC238043
| Job: | IRC238043 |
| Location: | Ukraine - Kyiv |
| Designation: | Lead Software Engineer |
| Experience: | 5-10 years |
| Function: | Engineering |
| Skills: | Apache Spark / PySpark, Azure, azure data factory, Azure Data Lake Store, Data Security, Databricks, MS SQL Server, SQL |
| Work Model: | Remote |
Description:
Our Client helps consumers find the cars they want – with the largest inventory of used and new cars available in Canada, and encourages better vehicle buying decisions through helpful reviews, buyers guides, pricing tools, and more.
Our Client accelerates dealers’ ability to market and manage their inventory through a powerful suite of solutions with leading Marketplaces, Web Solutions, Web Traffic, Inventory Management, Conversion Tools, and industry-leading customer service.
Project Overview:
The project aims to migrate the Collateral Management System (CMS) from its current vendor-hosted environment to the client’s in-house data platform. The CMS is a critical system that manages the collateral process, including valuation, monitoring, and reporting, which is essential for the organization’s risk management and regulatory compliance.
Requirements:
Technical Skills & Qualifications:
- SQL: Fluency with large and complex SQL queries for data processing.
- Data Lake/Data Lakeshouse: Familiarity with building Data Lakes and Data Lakehouses using the modern tools, technologies, and approaches.
- Databricks & PySpark: Proven experience in building and managing data pipelines using Databricks and PySpark.
- Data Vault 2.0 and Semantic modeling: Understanding of how to model the united data repository in an efficient manner.
- Azure Cloud: Hands-on experience with Azure Cloud services, particularly in data management.
- Azure Data Lake & ADSL Gen2: Proficient in managing data storage and processing within Azure Data Lake and ADSL Gen2 environments.
- Azure Data Factory: Extensive experience in designing and building data integration workflows using Azure Data Factory.
- Microsoft SQL Server: Experience in development, replication, and administration.
- Data Security: Experience with handling sensitive data, including PII and PCI, ensuring compliance with relevant regulations.
Desired Experience:
- Experience: 5+ years in data engineering or related roles with a focus on data migration, cloud platforms, and large-scale data processing.
- Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.
- Certifications (Nice to Have): Azure certifications such as Azure Data Engineer, Azure Solutions Architect, or equivalent.
Job Responsibilities:
As a Data Platform Migration Lead Engineer, you will play a crucial role in our Data Platform Migration project. You will be responsible for designing, developing, leading a team and implementing solutions to migrate our existing data assets and processes to a modern cloud-based platform, ensuring high levels of performance, security, and scalability.
Key Responsibilities:
- Design, develop, and implement data migration solutions for the Data Platform Migration project.
- Work closely with stakeholders to understand migration requirements and ensure seamless transition.
- Reverse engineer the existing Data Platform and define the target Data Platform setup.
- Setup ingestion of the data sources into the target Data Platform.
- Configure the required storage, compute, and management components and areas of the target Data Platform.
- Develop and maintain data pipelines to move, transform, and aggregate the data within the target Data Platform.
- Define the conceptual layout and entity segregation within the target Data Platform.
- Make the result data sets available for easy consumption and distribution from the target Data Platform.
- Implement data handling strategies to ensure compliance with PII and PCI data regulations.
- Collaborate with cross-functional teams to ensure the success of the migration project.
- Troubleshoot and resolve issues related to data migration and transformation.
- Monitor and optimize performance across the data platform.
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