ETL Data Engineer - Python, Snowflake & Cloud Pipelines

Socket.dev; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Hamilton Lane is seeking an ETL Data Engineer to design, develop, and maintain scalable data pipelines using Python (PySpark) and Snowflake. You’ll work with data architects and analysts to deliver high-quality data products for analytics and decision-making.

Focus areas include Snowflake data platforms, dimensional modeling, and data ingestion from diverse sources, with emphasis on data quality and governance.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, or equivalent experience.
• Experience developing data pipelines that extract, transform, and load data from REST APIs, databases, cloud storage, and files.
• Strong SQL skills and familiarity with Snowflake features like warehouses, streams, stages, and sharing.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines using Python (PySpark), Snowflake, and cloud-native tools.
• Build reliable ingestion, transformation, and loading processes for analytics and reporting needs.
• Model dimensional schemas and design scalable data warehouses within Snowflake.
• Ingest data from REST APIs, relational databases, SaaS apps, flat files, and cloud storage.
• Collaborate with data architects and stakeholders to deliver high-quality data products.
• Implement automated data quality controls and governance practices.

Skills

Python

Snowflake

SQL

Azure

ETL/ELT

Education

Bachelor’s degree in CS/IT/Data Engineering

Tools

Azure DevOps

Git

Power BI

Azure Data Factory

Job description

Hamilton Lane is seeking an ETL Data Engineer to design, develop, and maintain scalable data pipelines using Python (PySpark) and Snowflake. You’ll work with data architects and analysts to deliver high-quality data products for analytics and decision-making.

Focus areas include Snowflake data platforms, dimensional modeling, and data ingestion from diverse sources, with emphasis on data quality and governance.