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Mid-level Data Engineer in metro with broad AWS/Snowflake requirements drives high competition.
Core data engineering skills are transferable, but Snowflake and vector/graph requirements add moderate industry specificity.
Explicit 5–8 years plus mandatory AWS, Snowflake, Python, and CI/CD increases shortlisting strictness.
Design, develop, and maintain scalable data pipelines on AWS using services like S3, Glue, Lambda, Redshift, and EMR.
Build and optimize Snowflake data warehousing solutions including performance tuning and data modeling.
Collaborate with cross-functional teams to deliver efficient, scalable data solutions and ensure pipeline performance, data quality, integrity, and security.
5-8 years of experience in data engineering or related roles.
Strong hands-on experience with AWS cloud services focused on data and AI workloads.
Advanced proficiency in Python and SQL, plus deep experience with Snowflake architecture and performance tuning.
This position requires working onsite five days a week.
Experienced with graph and vector data modeling and practical application of related database technologies.
Skilled in developing and operating CI/CD pipelines (GitHub Actions) within Agile environments using Azure DevOps boards.
Familiarity with orchestration tools like AWS Step Functions and real-time data processing frameworks is a plus.