Smart IT Frame LLC
DBT Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a DBT Data Engineer in New York (onsite) with a contract length of "unknown." Pay rate is "unknown." Key skills include dbt Core, advanced SQL, data warehouse modeling, ELT on Databricks, and Git proficiency.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 29, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New York, United States
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🧠 - Skills detailed
#Snowflake #Databricks #Scala #dbt (data build tool) #Data Warehouse #Code Reviews #Delta Lake #Macros #Documentation #SQL (Structured Query Language) #"ETL (Extract #Transform #Load)" #Data Engineering #GIT
Role description
Role - DBT Data Engineer
Location - New York (Onsite)
Must-Have Skills:
• Hands-on expertise with dbt Core: model development, Jinja macros, sources, tests, documentation, and reusable packages
• Advanced SQL: complex transformations, query optimization, performance tuning
• Strong dimensional/data warehouse modeling: star/snowflake schemas, facts/dimensions, SCDs
• Experience with ELT on Databricks: Databricks SQL, Delta Lake, medallion architecture (bronze/silver/gold)
• Git proficiency: PR workflows, branching, code reviews, merge strategies, cherry-picking
• CI practices for dbt: build/test pipelines, enforce quality gates
• Ability to build scalable, maintainable, and well-documented models
Role - DBT Data Engineer
Location - New York (Onsite)
Must-Have Skills:
• Hands-on expertise with dbt Core: model development, Jinja macros, sources, tests, documentation, and reusable packages
• Advanced SQL: complex transformations, query optimization, performance tuning
• Strong dimensional/data warehouse modeling: star/snowflake schemas, facts/dimensions, SCDs
• Experience with ELT on Databricks: Databricks SQL, Delta Lake, medallion architecture (bronze/silver/gold)
• Git proficiency: PR workflows, branching, code reviews, merge strategies, cherry-picking
• CI practices for dbt: build/test pipelines, enforce quality gates
• Ability to build scalable, maintainable, and well-documented models
