Senior Data Engineer ID65436

AgileEngine•Downey, CA

2d•Hybrid

About The Position

We are looking for a Senior Data Engineer to design and maintain scalable data pipelines and platforms supporting Accounting, Finance, Payments, and Tax functions within a large-scale financial systems platform. You will build ETL/ELT workflows using Python, dbt, and Airflow, working across Snowflake, Redshift, Kafka, and Spark to ensure data availability and reliability for critical business operations. The role operates in a cross-functional Agile environment with direct stakeholder engagement across Engineering, Product, and Analytics teams.

Requirements

5+ years of professional experience in Data Engineering or Data Warehousing roles
Strong programming skills in Python
5+ years of experience building data pipelines using ETL/ELT tools, with dbt preferred
3+ years of hands-on experience with big data technologies such as Snowflake, Redshift, Kafka, Spark, or Hive
Extensive experience working with both SQL and NoSQL databases
Strong expertise with workflow orchestration and pipeline management tools such as Airflow
Strong understanding of scalable data architecture and engineering best practices
Excellent communication and stakeholder management skills
Proven ability to manage competing priorities and deliver within agreed sprint commitments
Comfortable working in highly collaborative, cross-functional Agile teams
Self-starter mindset with strong analytical, problem-solving, and critical-thinking abilities
Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field preferred, or Bachelor’s degree with relevant experience
Upper-intermediate English level

Nice To Haves

Experience working in cloud-based data environments
Familiarity with modern data warehousing and distributed systems concepts
Exposure to data governance, observability, or data quality frameworks

Responsibilities

Design, develop, and maintain scalable and reliable ETL/ELT pipelines
Build and optimize data workflows using tools such as dbt and Airflow
Develop and maintain data solutions leveraging technologies such as Snowflake, Redshift, Kafka, Spark, and Hive
Work with both SQL and NoSQL databases to support data ingestion, transformation, and analytics needs
Monitor, troubleshoot, and improve data pipeline performance, scalability, and reliability
Collaborate closely with cross-functional teams including Engineering, Product, Analytics, and Business stakeholders
Participate in Agile ceremonies and contribute to sprint planning and delivery commitments
Manage multiple priorities effectively while ensuring timely and high-quality deliverables
Contribute to data architecture discussions and help drive engineering best practices