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Global brand, mid-level role, metro location and broad AWS/Snowflake requirements make applicant competition high.
AWS, Snowflake, and Python are broadly transferable, while vector/graph DB experience moderately increases domain specificity.
Explicit 5-8 years plus mandatory AWS, Snowflake, Python and graph/vector DB requirements increase shortlisting strictness.
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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 cross-functionally to deliver efficient, reliable data solutions ensuring quality, integrity, and security throughout pipelines.
5-8 years of experience in data engineering or related roles.
Strong hands-on experience with AWS cloud services for data and AI workloads.
Advanced proficiency in Python and SQL; expertise with Snowflake and graph/vector data modeling.
Position requires working onsite five days a week; relocation is available.
Experienced in developing and operating CI/CD pipelines (e.g., GitHub Actions) and cloud native deployments.
Familiar with Agile environments using Azure DevOps (AzDO) for backlog management.
Skilled in troubleshooting, performance tuning, and collaborative cross-team work involving data scientists and business stakeholders.