Sr. Data Engineer

Premier Nutrition Company’s Enterprise Data Intelligence team is building and operating cloud-native data infrastructure that powers analytics, reporting, and data science across a SaaS-based environment. In this role, you will design, develop, and maintain scalable cloud data pipelines and ETL/ELT workflows, while helping strengthen Data Ops operational excellence and ensuring the data warehouse is administered, monitored, and optimized end to end.

This is a hybrid position based in Emeryville, CA with a focus on reliable production delivery, data quality, and continuous improvement for enterprise data assets.

• Design, build, and maintain cloud-native data pipelines and infrastructure to support analytics, reporting, and data science use cases in a SaaS-based environment.
• Own the full data lifecycle, from requirements gathering through design, ingestion, transformation, modeling, testing, deployment, monitoring, and production support.
• Implement and manage ELT pipelines using Fivetran
  , dbt
  , and Python to deliver reliable, scalable data solutions.
• Develop and maintain data models, data marts, APIs, and automation scripts to streamline analytics workflows.
• Design and deploy Snowflake database schemas, tables, and views across multiple environments including DEV
  , QA/UAT
  , and PROD
  .
• Work with stakeholders to translate business requirements into robust technical designs.
• Provide documentation, training, and enablement to support adoption of enterprise data assets.
• Protect pipeline reliability by monitoring production schedules, troubleshooting failures, and ensuring smooth execution.
• Monitor and investigate data quality issues and drive solutions when problems arise.
• Administer Snowflake security strategy and database management.
• Fine-tune and optimize databases, queries, and pipelines for performance and efficiency.
• Identify opportunities for automation to improve Enterprise Data Intelligence team effectiveness and operating efficiency.
• Research and test new technology solutions, including supporting assessment of architectural or database design changes needed for enterprise adoption.
• Follow documented procedures for database configuration, maintenance, upgrades, testing, and deployment.
• Ensure best practices for enterprise database administration to support user experience and sustainable design.

• Bachelor’s degree in computer science, Engineering, Information Systems, or a related technical discipline.
• 5+ years of experience as a Data Engineer in cloud-native and SaaS-based environments, supporting enterprise data platforms built on data warehouses, data lakes, and application databases (including Snowflake
  , Oracle
  , or SQL Server
  ).
• 7+ years of SQL experience, with proven ability to write, optimize, and maintain complex queries and stored procedures.
• Programming experience in Python
  , Scala
  , or other scripting languages for automation, data transformation, and integration.
• Hands-on experience building ELT/ETL pipelines and dimensional models/data marts using modern tools such as dbt
  , Fivetran
  , or equivalent frameworks.
• Demonstrated experience with SaaS data sources and APIs, including integrations from ERP (e.g.,
  Net Suite
  ), CRM, and planning tools (e.g.,
  Oracle EPM
  , O9
  , or Salesforce
  ).
• Exposure to or hands-on experience with AI/LLM frameworks such as Snowflake Cortex or MCP to enhance automation and data intelligence.
• Experience with data integration and orchestration tools such as Dell Boomi
  , Airflow
  , Azure Data Factory
  , or equivalent tools.
• Strong understanding of data governance, observability, lineage, and data quality management in a cloud environment.
• Experience with Git
  -based version control (e.g., Git Hub, Git Lab) and CI/CD workflows for deployment across DEV, QA, and PROD.
• Working knowledge of cloud platforms (preferably Azure
  , with exposure to AWS or GCP
  ) and secure data exchange mechanisms (APIs,
  SFTP
  ).
• Excellent communication and collaboration skills for cross-functional work in a distributed SaaS organization.
• Willingness to be on call for night/weekend production support.
• Ability to think strategically about tools and technologies that enable analytics.