Senior AWS Data Engineer

Company Description

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Job Description

We are seeking a skilled and motivated Senior Data Engineer to join our growing data team. In this role, you will be responsible for designing, building, and optimizing scalable data pipelines and cloud-based data infrastructure. You will work extensively with AWS services and Snowflake to power analytics, reporting, and AI/ML use cases across the organization.

This is a hands-on role requiring strong data engineering fundamentals, cloud-native experience, and a drive to build high-performance data systems.

Responsibilities

• Design and develop scalable ETL/ELT data pipelines using AWS Glue, Lambda, Step Functions, and Python.

• Build and maintain data lakes and data warehouses using AWS S3, Redshift, and Snowflake.

• Model and transform data for analytics and reporting using SQL and Snowflake best practices.

• Implement monitoring, logging, and alerting for data pipelines to ensure reliability and performance.

• Collaborate with Data Analysts, Data Scientists, and Product teams to understand data needs and deliver high-quality datasets.

• Ensure data governance, quality, and security across data pipelines and storage layers.

• Participate in code reviews, performance tuning, and cloud cost optimization efforts.

Qualifications

• 5+ years of hands-on experience in Data Engineering.

• Strong expertise in AWS services for data processing – including Glue, S3, Redshift, Athena, and Lambda.

• In-depth experience with Snowflake, including data modeling, query optimization, and access control.

• Proficiency in SQL, Python, and Spark for data processing and transformation.

• Solid understanding of data architecture, data governance, and security best practices.

• Familiarity with CI/CD processes, version control (Git), and Infrastructure-as-Code tools like Terraform or CloudFormation.