Data Engineer II

4+ years working in a data engineering role that supports incoming/outgoing products supporting internal and external customers 2+ years demonstrated expertise in designing an end-to-end data pipeline in cloud frameworks (such as GCP, AWS, Azure) with requirements from multiple stakeholders 2+ years of Python or similar experience writing efficient, testable, and readable code 2+ years experience in building streaming data ingestion pipelines 1+ year of ML (Machine Learning) support and implementation or MLOps Deep SQL experience with columnar databases such as Google BigQuery, Snowflake, or Amazon Redshift Demonstrated experience with AI Coding assistants Experience building CICD pipelines for data processing using tools such as Docker, CircleCI, dbt, git, etc Able to manage infrastructure using IAC tools such as Terraform or Pulumi Experience with common data orchestration tools such as Apache Airflow (or similar) to manage SLOs and processing dependencies Experience with creating alerts and monitoring pipelines which contribute to overall data governance Experience with containerization and container orchestration technologies with cloud architecture and implementation features (single- and multi-tenancy, orchestration, elastic scalability) Familiarity with standard IT security practices such as identity and access management (IAM), data protection, encryption, certificate, and key management

Build robust solutions for transferring data from first and third-party applications to and from our data warehouse Develop hardened and repeatable (CI/CD) data models and pipelines to enable reporting, modeling and machine learning Improve data availability to our enterprise clients through a mix of traditional push delivery, cloud, and event-driven (eg: API, grpc) data sharing methods Ensure data quality through automated monitoring and alerting, and occasionally serving within an on-call rotation Leverage Google Cloud (GCP) tools (eg: Cloud Run, Cloud Functions, Vertex AI, App Engine, Cloud Storage, IAM, etc.) and services (eg: Astronomer - Apache Airflow) to bring data workloads to production Drive and support MLOps to improve Data Science monitoring and governance Enable and support Generative AI (eg: LLM) pipelines, allowing internal teams to quickly prototype Collaborate with product, engineering, stakeholders and data teams to deliver informed solutions to platform and client needs