ETL Migration & Pipeline Development in , us

ETL Migration & Pipeline Development

Duration: 2?Year Contract
Location: 100% Remote
Interview Process: One virtual interview (one?and?done)
Data Engineer - ETL Migration & Pipeline Development

We are seeking a Data Engineer contractor (100% remote) to support and execute the migration of ETL pipelines from Matillion to Apache Airflow. This role focuses on rebuilding pipelines, improving reliability, and enabling scalable, code-based data workflows. This is a hands-on role requiring someone who can ramp quickly, work with existing ETL logic, and contribute within the first 12 weeks.
Responsibilities:

Migrate existing ETL pipelines from Matillion to Apache Airflow while preserving logic and dependencies
Develop and maintain Airflow DAGs with proper scheduling, retries, and failure handling
Reverse engineer existing Matillion jobs and translate them into Python-based workflows
Build and optimize data pipelines across systems such as S3, Snowflake, and relational databases
Perform data validation and reconciliation between source and target systems
Write and optimize SQL transformations for large-scale datasets
Implement monitoring, alerting, and error handling for pipelines
Collaborate with data, platform, and analytics teams to ensure smooth migration and deployment
Document pipelines, workflows, and operational processes
Required Qualifications:

3+ years of experience in Data Engineering or ETL development
Strong hands-on experience with Apache Airflow or similar orchestration tools
Proficiency in Python for building data pipelines and workflows
Strong SQL skills and experience working with large datasets
Experience with cloud platforms (AWS : S3, RDS/Aurora, IAM)
Experience with cloud data warehouses (Snowflake or similar)
Experience building and maintaining ETL/ELT pipelines
Familiarity with Git and CI/CD workflows
Qualifications:

Experience migrating ETL workflows from tools like Matillion, Informatica, Talend, or SSIS to Airflow
Experience with Airflow in containerized environments (Docker, Kubernetes/EKS)
Familiarity with data validation, reconciliation, and pipeline testing strategies
Experience with monitoring tools such as Datadog or CloudWatch
Understanding of data modeling or medallion architecture