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Mid-level generalist data engineer, PST/remote hours, and broad GCP/ETL requirements increase competitive density.
Core data engineering skills are transferable across industries, though finance month-end and GCP specifics add moderate specialization.
Multiple mandatory skills (GCP, BigQuery, Airflow, Terraform, Python), explicit 3–5 years and PST availability raise filter strictness.
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Design, develop, and maintain scalable data extraction and ELT pipelines in Google Cloud Platform (GCP) integrating enterprise systems like Salesforce, Jira, NetSuite, leveraging REST/SOAP APIs and Python.
Build and manage infrastructure as code with Terraform, maintain CI/CD pipelines, and support scheduled production jobs including month-end finance close cycles in PST timezone.
Develop AI agents and automation tools to optimize workflows and improve productivity within the Finance Data Engineering team.
Bachelor's or master's degree in Computer Science, Computer Engineering, or related technical discipline.
3 to 5 years of hands-on data engineering experience with expertise in data warehousing and ETL/ELT pipelines.
Strong proficiency in Google Cloud Platform services including BigQuery, Dataform, Cloud Run Functions, Workflows, Pub/Sub, and infrastructure as code using Terraform.
Availability to work in the PST timezone to support production and month-end close cycles.
Experienced in building and maintaining complex data pipelines integrating multiple SaaS platforms with expertise in Python and API data extraction.
Capable of managing CI/CD pipelines and infrastructure as code, working independently with strong technical documentation skills in cloud-based environments.
Skilled in developing AI-powered tools for data engineering automation and supporting finance-specific reporting and analytics workflows.