Senior Data Engineer - AWS

Fractal is a strategic AI partner to Fortune 500 companies, aiming to enhance human decision-making in enterprises. They are seeking a Senior Data Engineer - AWS to design and build scalable data pipelines on AWS, guide client teams through data platform best practices, and act as a technical consultant to solve complex data challenges.

Responsibilities:

• Design, build, and maintain scalable data pipelines using AWS services such as S3, Lambda, Step Functions, Glue (Serverless), and EventBridge, landing curated data in Snowflake
• Develop and optimize ETL/ELT workflows using PySpark, Parquet file formats, and Apache Iceberg table formats
• Build and maintain semantic layers in Snowflake to enable consistent, governed, business-ready analytics
• Manage metadata and data discovery using AWS Glue Catalog and Glue Crawlers, and enable analytics through Athena
• Implement event-driven architectures with SNS and EventBridge for data processing and notifications
• Enable secure data lake operations using AWS Lake Formation for access control and governance
• Support data ingestion and migration initiatives, including migrating data from Teradata to S3
• Ensure reliable deployments and infrastructure automation using uDeploy, CloudFormation, Terraform, CDK, and GitHub
• Collaborate on cost reporting, monitoring, and optimization, and research emerging AWS features (e.g., S3 Tables) to improve performance, scalability, and cost efficiency
• Act as a hands-on subject-matter expert and technical consultant for client stakeholders and their engineering teams
• Mentor and upskill client engineers, sharing best practices for AWS and Snowflake data engineering and raising the technical bar across the team
• Partner with cross-functional teams to translate business problems into robust, scalable, end-to-end data solutions

Requirements:

• Expert-level data engineer with extensive, hands-on experience designing and building production data platforms on AWS
• Proven ability to build end-to-end data pipelines from the ground up—ingestion, transformation, and delivery—not just maintain existing ones
• Hands-on experience designing and building semantic layers in Snowflake
• Track record of acting as a technical subject-matter expert or consultant, advising and upskilling client teams and engineering stakeholders
• Comfortable being the most senior technical voice in the room and translating complex engineering concepts for both technical and business audiences
• Excellent problem-solving skills and the ability to collaborate effectively across cross-functional teams
• Self-directed and proactive, able to balance multiple workstreams with a focus on quality and delivery
• Deep, hands-on expertise with core AWS data services: S3, Lambda, Step Functions, Glue (Serverless), Glue Catalog, Glue Crawlers, Athena, SNS, EventBridge, and Lake Formation
• Strong proficiency in PySpark and building production ETL/ELT workflows, including with columnar and open table formats such as Parquet and Apache Iceberg
• Hands-on Snowflake experience, including designing and building semantic layers
• Practical knowledge of infrastructure-as-code and CI/CD tooling: CloudFormation, Terraform, CDK, uDeploy, and GitHub
• Experience with large-scale data migrations (e.g., migrating from Teradata to S3)
• Strong understanding of cost management, monitoring, and reporting in AWS environments
• Working knowledge of data lake architectures, metadata management, and data governance
• Bachelor's degree with a quantitative or technical emphasis: Computer Science, Engineering, Data Science, Mathematics, Information Systems, or a related field
• Extensive hands-on data engineering experience, typically 7+ years, with deep expertise in AWS and Snowflake
• Master's degree desirable