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Mid-level, metro-based Data Engineer with common stack (Snowflake, dbt, Python) attracts high applicant competition.
Core data engineering skills are transferable across industries despite banking domain preference.
Multiple mandatory technical skills and explicit 5+ years make filters stringent.
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Design and build data ingestion pipelines and data platforms to support Enterprise Data Warehouse, Operational Data Store, and Data Marts.
Develop reusable data pipelines and frameworks using Python, leveraging technologies including Snowflake, DBT, and AWS cloud services.
Deliver performance tuning, impact analysis of changes, and provide technical support for production incidents while collaborating in an Agile environment with global teams.
Minimum 5+ years experience in data engineering with expertise in building data pipelines for Data Warehouse and Data Lake platforms.
Strong technical knowledge of Snowflake data platform and ELT tools such as DBT; proficiency in SQL, Python scripting, and Unix.
Experience with AWS cloud services including S3, Lambda, Glue, Step Functions, and CloudWatch.
Work Experience Required: 5+ years in relevant data engineering roles.
Experienced working with enterprise-scale data platforms and migrating legacy/on-prem systems to cloud-based solutions using Snowflake and AWS.
Comfortable operating in a global, Agile environment coordinating with cross-functional and remote US teams.
Skilled in performance tuning, data modeling (logical and physical), and managing complex data pipelines with CI/CD and version control (GitLab).