Data Engineer: Databricks & Snowflake Expert

Machine Learning Technologies, LLC; Dallas (TX); On-site; USD 100,000 - 150,000; Full time

Job summary

Machine Learning Technologies, LLC is seeking a Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data platforms that support enterprise data warehousing and analytics initiatives.

You will build reliable data integration solutions, implement ETL/ELT processes with Databricks and SQL, and collaborate with BI teams to optimize data quality, modeling, and performance across AWS, Azure, or Google Cloud environments.

Qualifications

• Experience designing, developing, and maintaining scalable data pipelines.
• Strong knowledge of data warehousing concepts and architectures.
• Proficiency in building data integration solutions and data quality checks.

Responsibilities

• Design, develop, and maintain scalable ETL and ELT data pipelines.
• Build and optimize data processing workflows using Databricks.
• Develop data warehouse solutions using Snowflake.
• Implement data ingestion and transformation processes.
• Work with structured and unstructured data from multiple sources.
• Develop complex SQL queries and optimize database performance.
• Implement data quality checks and validation procedures.
• Integrate data from cloud platforms and enterprise applications.
• Collaborate with analytics and business intelligence teams.
• Monitor pipeline performance and troubleshoot data processing issues.

Skills

Python

PySpark

SQL

ETL/ELT

Cloud platforms

Data modeling

Delta Lake

Apache Airflow

Tools

Databricks

Snowflake

Apache Spark

Airflow

Job description

Machine Learning Technologies, LLC is seeking a Data Engineer to design, develop, and maintain scalable data pipelines and cloud-based data platforms that support enterprise data warehousing and analytics initiatives.

You will build reliable data integration solutions, implement ETL/ELT processes with Databricks and SQL, and collaborate with BI teams to optimize data quality, modeling, and performance across AWS, Azure, or Google Cloud environments.