Design, develop, and maintain scalable and efficient ETL/ELT pipelines using tools such as AWS Glue, DBT, and Airflow.
Build and automate event-driven data workflows leveraging AWS Lambda, EventBridge, and related services.
Integrate data replication tools like Hevo or Fivetran for seamless ingestion from external systems.
Perform data modeling (dimensional and normalized) to support Tableau team and analytics.
Manage infrastructure as code using Terraform and automate workflows using GitHub Actions.
Optimize and maintain Snowflake data warehouse environments, including performance tuning and data lifecycle management.
Use GitHub Actions to deploy DBT projects, manage data pipelines, and automate testing/monitoring tasks.
Collaborate with analysts, data scientists, and engineering teams to gather requirements and deliver robust data solutions.
Ensure data quality, integrity, and consistency across all systems and processes.
Containerize data workflows using Docker for portability and repeatability.
Productionalizes SQL queries from data analysts into structured dbt models within the silver/gold layer

4–6 years of experience as a Data Engineer or in a similar role.
Hands-on experience with AWS data services: Glue, Lambda, EventBridge, S3, IAM, Airflow/MWAA
Proficiency in SQL and Python for data manipulation and orchestration.
Snowflake data warehouse design and query optimization
Solid experience with DBT, Apache Airflow, and Snowflake (or other cloud data warehouses).
Experience using GitHub Actions for CI/CD in data workflows.
Familiarity with data replication tools such as Hevo or Fivetran.
Strong understanding of data modeling principles (e.g., star/snowflake schema).
Prior experience in eCommerce, retail, or high-volume transactional data environments.
Infrastructure-as-Code expertise using Terraform.
Experience containerizing pipelines with Docker, ECR and EKS.
Strong problem-solving and communication skills in a collaborative, cross-functional environment.

Senior Data Engineer

Key skills