Data Engineer (ETL Pipeline)

Job Description

The Data Engineer will collaborate closely with the Airflow Specialist and the Data Architect to maintain alignment with the overall data architecture while focusing on delivering high-performance data pipelines.

1. ETL Pipeline Development & Automation:

• Design, develop, and automate ETL pipelines to migrate data from legacy systems to modern platforms.
• Leverage tools such as AWS Glue, dbt, and Python to support data ingestion, transformation, and integration.
• Maintain high-quality, scalable pipelines capable of processing large datasets efficiently.

1. Data Transformation & Integration:

• Implement data transformation workflows to prepare data for analytical workloads in Snowflake and PostgreSQL environments.
• Ensure data consistency and integrity during transformations by leveraging best practices in schema enforcement and validation.

1. Collaboration & Alignment:

• Work closely with the Airflow Specialist to align ETL workflows with orchestration pipelines.
• Collaborate with the Data Architect to ensure data models and pipelines align with overall system design and project goals.

1. Process Optimization:

• Identify opportunities for optimizing ETL processes to enhance performance and reduce costs.
• Implement change data capture (CDC) mechanisms and incremental updates to ensure timely data availability.

1. Data Quality & Monitoring:

• Support the integration of automated validation tools, such as Great Expectations, into ETL workflows.
• Monitor ETL processes to ensure consistent performance and timely issue resolution.

• 8+ years of experience in data engineering, with a focus on building and automating ETL pipelines.
• Hands-on expertise with AWS Glue, Python, and dbt for data transformation and pipeline development.
• Proven experience integrating data workflows into Snowflake and PostgreSQL.
• Strong understanding of modern data architecture principles and ETL best practices.
• Familiarity with data governance tools such as DataHub and validation frameworks like Great Expectations.
• 5+ years of experience in data engineering, with a focus on building and automating ETL pipelines.
• Hands-on expertise with AWS Glue, Python, and dbt for data transformation and pipeline development.
• Proven experience integrating data workflows into Snowflake and PostgreSQL.
• Strong understanding of modern data architecture principles and ETL best practices.
• Familiarity with data governance tools such as DataHub and validation frameworks like Great Expectations.

• Experience with change data capture (CDC) and real-time data processing mechanisms.
• Familiarity with Apache Spark for large-scale data transformation tasks.
• Proficiency in integrating ETL workflows into Airflow orchestrations.
• Strong analytical skills for identifying performance improvement opportunities in data workflows.