Senior Data Engineer
Irvine, CA
Fulltime

Requirements

• Proven experience as a Data Engineer with hands-on experience in AWS.
• Proficiency in Python, Glue, S3, Airflow, DBT, Redshift, RDS or equivalent experience.
• Strong understanding of agile methodologies.
• Expertise in data pipeline build-out and ETL processes.
• Excellent analytical and problem-solving skills.
• Strong communication and interpersonal skills.
• Ability to work effectively in a fast-paced environment.

Key Skills
Python, Glue, S3, Airflow, DBT, Redshift, RDS

Responsibilities

• Craft and develop robust data pipelines and ETL processes using AWS services.
• Conduct comprehensive data analysis to derive actionable insights and inform business decisions.
• Collaborate closely with cross-functional teams to gather and refine requirements.
• Ensure data quality and integrity through rigorous testing and validation.
• Maintain and optimize data infrastructure for performance and scalability.
• Provide thought leadership and solicit diverse opinions to improve project outcomes.
• Monitor and evaluate the progress of data initiatives against goals and timelines.

Job Type: Full-time
Pay: $138,772.00 - $148,791.00 per year

Benefits:

• 401(k)
• Dental insurance
• Health insurance

Schedule:

• Monday to Friday

Experience:

• DBT: 2 years (Required)
• Python: 6 years (Required)
• ETL: 7 years (Required)
• AWS: 5 years (Required)

Ability to Commute:

• Irvine, CA 92602 (Required)

Ability to Relocate:

• Irvine, CA 92602: Relocate before starting work (Required)

Work Location: In perso

• Craft and develop robust data pipelines and ETL processes using AWS services.
• Conduct comprehensive data analysis to derive actionable insights and inform business decisions.
• Collaborate closely with cross-functional teams to gather and refine requirements.
• Ensure data quality and integrity through rigorous testing and validation.
• Maintain and optimize data infrastructure for performance and scalability.
• Provide thought leadership and solicit diverse opinions to improve project outcomes.
• Monitor and evaluate the progress of data initiatives against goals and timelines