Data Engineer

• Collection of performance and operational data from customer product deployment and management platforms.
• Enabling analytics to understand feature utilization and customer satisfaction.
• Supporting proactive risk mitigation campaigns and customer enablement through targeted communications.
• Secure collection and handling of telemetry data with customer privacy considerations. 

Your Impact 

• Design, develop, and implement scalable and efficient data pipelines using AWS services (e.g., S3, EC2, ECS, Aurora).
• Write high-quality, maintainable Python code for data extraction, transformation, and loading (ETL) and Machine leaning / AI (LLM) processes.
• Develop and integrate with Web Services, Micro Services, and APIs to ingest and expose data from various internal and external sources.
• Implement robust Data Engineering solutions, including data modeling, database design, and data warehousing.
• Optimize data storage, retrieval, and processing for performance and cost-efficiency on AWS.
• Monitor and troubleshoot data pipeline issues, ensuring data quality, integrity, and availability.
• Collaborate with data scientists, analysts, and software engineers to understand data requirements and deliver solutions.
• Contribute to the development and enforcement of data governance policies and standards.
• Stay up-to-date with emerging technologies and industry trends in Data Engineering, cloud computing, and data management. 

Minimum Qualifications:

• Proven experience in Data Engineering roles, with a strong portfolio of data pipeline development.
• Proficiency in Python for data manipulation, automation, and scripting.
• 4 years of professional experience in data engineering or a similar role.
• Extensive experience with cloud platforms, particularly AWS, including services relevant to data storage, processing, and analytics.
• Solid understanding and practical experience with designing and consuming Web Services and APIs.
• Familiarity with Micro Services architecture and its implications for data integration.
• Experience with relational and NoSQL databases.
• Strong understanding of data warehousing concepts and best practices.
• Ability to work independently and as part of a collaborative team.
  • Strong communication and interpersonal skills.