Remote Principal Data Engineer

Summary

Join Seamless.AI as a Principal Data Engineer and contribute to delivering the world's best sales leads by designing, developing, and maintaining robust ETL pipelines. The ideal candidate will have expertise in Python, Spark, AWS Glue, and other ETL technologies.

Requirements

• Strong proficiency in Python and experience with related libraries and frameworks (e.g., pandas, NumPy, PySpark)
• Hands-on experience with AWS Glue or similar ETL tools and technologies
• Solid understanding of data modeling, data warehousing, and data architecture principles
• Expertise in working with large data sets, data lakes, and distributed computing frameworks
• Experience developing and training machine learning models
• Strong proficiency in SQL
• Familiarity with data matching, deduplication, and aggregation methodologies
• Experience with data governance, data security, and privacy practices
• Strong problem-solving and analytical skills, with the ability to identify and resolve data-related issues
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams
• Highly organized and self-motivated, with the ability to manage multiple projects and priorities simultaneously
• Bachelor's degree in Computer Science, Information Systems, related fields or equivalent years of work experience
• 7+ years of experience as a Data Engineer, with a focus on ETL processes and data integration
• Professional experience with Spark and AWS pipeline development required

Responsibilities

• Design, develop, and maintain robust and scalable ETL pipelines to acquire, transform, and load data from various sources into our data ecosystem
• Collaborate with cross-functional teams to understand data requirements and develop efficient data acquisition and integration strategies
• Implement data transformation logic using Python and other relevant programming languages and frameworks
• Utilize AWS Glue or similar tools to create and manage ETL jobs, workflows, and data catalogs
• Optimize and tune ETL processes for improved performance and scalability, particularly with large data sets
• Apply methodologies and techniques for data matching, deduplication, and aggregation to ensure data accuracy and quality
• Implement and maintain data governance practices to ensure compliance, data security, and privacy
• Collaborate with the data engineering team to explore and adopt new technologies and tools that enhance the efficiency and effectiveness of data processing