Sr. AWS Data Engineer

Job Title: Sr. AWS Data Engineer Location: [Remote] Must have: – Snowflake, dbt, and Fivetran experience is required, along with 15+ years of retail industry background Job Summary Albertsons is seeking a highly skilled AWS Data Engineer with strong hands-on experience in Snowflake, DBT, and Fivetran to build and optimize modern cloud data pipelines and analytics platforms. The ideal candidate will have deep expertise in designing scalable ELT/ETL frameworks on AWS and implementing data transformation workflows using DBT while leveraging Fivetran for data ingestion. Key Responsibilities • Design, develop, and maintain scalable data pipelines using AWS services • Implement ELT processes using Fivetran for ingesting data from multiple sources into Snowflake • Develop and manage data transformation models using DBT • Build and optimize Snowflake data warehouse architecture, schemas, and performance tuning • Work with large datasets and ensure data quality, reliability, and integrity • Develop reusable data models and transformation frameworks • Monitor, troubleshoot, and optimize data workflows and performance • Collaborate with data analysts, BI teams, and stakeholders to deliver data solutions • Implement data governance, security, and best practices • Automate deployment using CI/CD pipelines Required Skills • 7+ years of experience in Data Engineering • Strong hands-on experience with AWS (S3, Glue, Lambda, Redshift, IAM, CloudWatch) • 3+ years of experience with Snowflake • Strong experience with DBT for data transformations • Hands-on experience with Fivetran or similar ELT tools • Expertise in SQL and performance optimization • Experience building data lakes and data warehouses • Knowledge of Python for data engineering tasks • Experience with orchestration tools like Airflow • Understanding of data modeling (Star/Snowflake schema) • Experience with CI/CD and DevOps practices • Excellent troubleshooting and performance tuning skills Preferred Skills • Experience in Retail / Grocery domain (highly preferred for Albertsons)