Data Engineer/ Snowflake Developer - LOCALS to Los Angeles, CA - W2 ONLY

Required Skills & Experience
5+ years of Data Engineering experience (7+ years preferred). Strong recent hands-on Snowflake experience. Advanced SQL development experience. Advanced Python development experience. Experience building and supporting data pipelines and ELT/ETL processes. Experience with Snowflake Tasks and Streams. Experience developing and maintaining data ingestion workflows. Experience with Airflow for workflow orchestration and scheduling. Strong data modeling experience. Experience with dimensional modeling and data warehouse concepts. Experience building analytics-ready or curated data layers. Experience supporting production environments and troubleshooting data issues.
Job Description
We are seeking a Senior Data Engineer to join a delivery team supporting enterprise data and analytics initiatives. This role is heavily focused on hands-on Snowflake development, analytics engineering, data modeling, pipeline development, and production support. The ideal candidate has strong Snowflake experience, advanced SQL and Python skills, and can translate technical requirements into scalable data solutions while contributing to design discussions and implementation.
Day-to-Day Responsibilities Monitor and support production data pipelines and deployed code. Identify, triage, troubleshoot, and resolve production issues and failed pipeline runs. Review business requirements, technical requirements, and design documentation. Translate requirements into scalable and reliable data engineering solutions. Build and maintain Snowflake data pipelines and ELT workflows. Develop and manage Snowflake databases, schemas, tables, views, stages, Tasks, and Streams. Create complex SQL transformations to prepare and model data for downstream analytics and reporting. Develop and support data ingestion and data loading processes into Snowflake. Utilize Airflow for orchestration, scheduling, and monitoring of data workflows. Build and maintain curated analytics datasets and reporting layers. Participate in solution design discussions and contribute recommendations around data architecture and best practices. Support testing, deployments, and post-production activities.