Scout 1 Solutions
Senior Data Engineer (Contractor - Hybrid, Houston, TX)
Core Requirements:
Python: Advanced proficiency for scripting, ETL/ELT, and pipeline automation. Snowflake: Hands-on experience with query tuning, security (RBAC, dynamic masking), data sharing, and cost optimization.
Key Responsibilities:
Design and optimize scalable data pipelines (batch/real-time) using Python, SQL, and Snowflake Build and manage Snowflake data warehouses/lakes, ensuring performance and governance. Implement row-level security and dynamic masking in Snowflake for PII/financial data. Monitor Snowflake credit consumption, warehouse usage, and query performance. Collaborate with data scientists/analysts to support ML/analytics initiatives (e.g., feature engineering). Develop semantic layers and data models (Star/Snowflake schemas) for analytics. Automate pipelines using CI/CD (Jenkins, GitHub) and ETL/scheduling tools (e.g., Airflow, dbt). Enable secure data sharing via Snowflake shares/reader accounts.
Technical Skills:
Python (ETL, scripting, automation). Snowflake (architecture, security, performance tuning). Advanced SQL (query optimization, complex transformations). ETL/ELT tools (e.g., Airflow, dbt, Informatica). Nice-to-Have:
ML/AI/LLM model integration. Data observability tools (e.g., Great Expectations). Metadata management (DataHub, Collibra). Soft Skills:
Strong communication to bridge technical and business teams. Problem-solving for data quality and pipeline scalability.
Python: Advanced proficiency for scripting, ETL/ELT, and pipeline automation. Snowflake: Hands-on experience with query tuning, security (RBAC, dynamic masking), data sharing, and cost optimization.
Key Responsibilities:
Design and optimize scalable data pipelines (batch/real-time) using Python, SQL, and Snowflake Build and manage Snowflake data warehouses/lakes, ensuring performance and governance. Implement row-level security and dynamic masking in Snowflake for PII/financial data. Monitor Snowflake credit consumption, warehouse usage, and query performance. Collaborate with data scientists/analysts to support ML/analytics initiatives (e.g., feature engineering). Develop semantic layers and data models (Star/Snowflake schemas) for analytics. Automate pipelines using CI/CD (Jenkins, GitHub) and ETL/scheduling tools (e.g., Airflow, dbt). Enable secure data sharing via Snowflake shares/reader accounts.
Technical Skills:
Python (ETL, scripting, automation). Snowflake (architecture, security, performance tuning). Advanced SQL (query optimization, complex transformations). ETL/ELT tools (e.g., Airflow, dbt, Informatica). Nice-to-Have:
ML/AI/LLM model integration. Data observability tools (e.g., Great Expectations). Metadata management (DataHub, Collibra). Soft Skills:
Strong communication to bridge technical and business teams. Problem-solving for data quality and pipeline scalability.