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Data Engineer with DBT & Databricks ,SQL -- 100% Remote -- Only independent Visa(W2 Only )

Trebecon LLCUnited States🇺🇸United StatesPosted 14 Sept 2026

Why This Role Stands Out

This fully remote Data Engineer role offers incredible growth by allowing you to master cutting-edge tools like DBT and Databricks, shaping impactful data solutions. You'll thrive here if you're a skilled SQL user with a passion for building robust data pipelines and collaborating with dynamic teams. Apply now to leverage your expertise and advance your career from anywhere!

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
United States
Posted
18 hours ago
SQLAWSETLSnowflakeAirflowAzureBigQueryDatabricksGitGoogle CloudPythonRedshiftdbt

Job Description

Job Title: Data Engineer – DBT, Databricks & SQL Expert

Location: Remote

Key Responsibilities:
  • Design, build, and maintain scalable and efficient ETL/ELT pipelines using DBT and Databr icks.
  • Develop, optimize, and troubleshoot complex SQL queries for data  transformations, validations, and reporting.
  • Collaborate with data analysts, data  scientists, and business stakeholders to understand data needs.
  • Implement data quality and dat a governance best practices in pipelines.
  • Work with structured and semi-structured data from multiple sources (e.g., APIs, flat files, cloud storage).
  • Build and maintain data models (star/ snowflake schemas) to support analytics and BI tools.
  • Monitor pipeline performance and troubleshoot issues in production environments.
  • Maintain version control, testing, and CI/CD for DBT projects using Git and DevOps pipelines.



Required Skills & Experience:
  • experience as a Data Engineer
  • Strong experience with DBT (Cloud or Core) for transformation workflows.
  • Proficiency in SQL — deep understanding of joins, window functions, CTEs, and performance tuning.
  • Hands-on experience with Databricks (Spark, Delta Lake, Notebooks).
  • Experience with at least one cloud data platform: AWS (Redshift), Azure (Synapse), or Google Cloud Platform (BigQuery).
  • Familiarity with data lake and lakehouse  architecture.
  • Experience with Git and version control in data projects.
  • Knowledge of orchestration tools like Airflow, Azure Data Factory, or dbt Cloud scheduler.
  • Comfortable with Python or PySpark for data manipulation (bonus).

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