Senior DBT Developer—Data Pipelines & Snowflake

Luxoft Germany; United States; Remote; USD 110,000 - 170,000; Full time

Job summary

Luxoft Germany is seeking a DBT Developer to design, develop, and maintain data transformation pipelines using DBT, Snowflake, and SQL in a US-based environment. You will build scalable ETL/ELT flows, implement governance and quality checks, and collaborate with data engineers and analysts to meet analytics needs.

You will apply best practices in data warehousing, optimize models for performance, and contribute to documentation, security, and continuous improvement in data engineering workflows.

Qualifications

• DBT, Snowflake, and SQL expertise required.
• 5+ years of experience in data engineering or analytics.
• Knowledge of workflow orchestration tools like Tidal.
• Experience with Python or scripting languages is a plus.
• Familiarity with Azure cloud platforms.
• Exposure to DevOps practices and CI/CD pipelines for data engineering.

Responsibilities

• Design, develop, and maintain DBT models, transformations, and SQL code for analytics data pipelines.
• Build ETL/ELT pipelines using DBT and Snowflake data sources.
• Define data modelling best practices and data warehousing techniques.
• Create complex SQL queries within DBT for incremental models and performance.
• Implement data governance, quality checks, and data dictionaries.
• Collaborate with data engineers, analysts, and stakeholders to meet requirements.
• Optimize DBT models to improve query performance and scalability.
• Maintain documentation and support data security and privacy controls.
• Stay updated on DBT features and industry best practices.
• Provide training and guidance to teammates on using DBT effectively.

Skills

SQL

Python/Scripting

CI/CD

Data governance

Tools

DBT

Snowflake

Azure

Job description

Luxoft Germany is seeking a DBT Developer to design, develop, and maintain data transformation pipelines using DBT, Snowflake, and SQL in a US-based environment. You will build scalable ETL/ELT flows, implement governance and quality checks, and collaborate with data engineers and analysts to meet analytics needs.

You will apply best practices in data warehousing, optimize models for performance, and contribute to documentation, security, and continuous improvement in data engineering workflows.