Mid-Senior Data Engineer - SQL, Python, dbt & Snowflake

Mid-Senior Data Engineer - SQL, Python, dbt & Snowflake
City of Industry, CA | Full-Time
Our client is a growing organization seeking a Data Engineer to join its expanding technology team. This is a hands-on role focused on building and maintaining production data solutions, supporting critical data operations, and improving the reliability and scalability of the overall data environment.
The ideal candidate is a strong hands-on engineer who enjoys working with production systems, troubleshooting unfamiliar problems, and taking ownership of data pipelines and operational processes.
What You'll Be Doing

• Design, build, and maintain production-grade data pipelines and transformations
• Develop and maintain reliable data models supporting reporting, analytics, and business applications
• Work extensively with SQL, Python, dbt, and cloud-based data technologies
• Own day-to-day production data operations, including orchestration, scheduled refreshes, monitoring, and alerting
• Troubleshoot pipeline failures, data quality issues, and other production incidents
• Support database administration activities, including user access, roles, permissions, performance, and maintenance
• Manage data deployments and contribute to CI/CD and infrastructure automation
• Investigate unfamiliar or poorly documented data sources and determine how systems, schemas, and integrations function
• Build data solutions supporting reporting, billing, customer, and operational use cases
• Integrate and work with data from internal, third-party, and legacy systems
• Partner with engineering, product, operations, and business teams to translate requirements into technical solutions
• Participate in code reviews and contribute to engineering standards and best practices
• Improve documentation, knowledge sharing, and overall platform reliability

Required Qualifications

• Strong SQL skills, including complex joins, window functions, aggregations, NULL handling, and understanding of data grain
• Hands-on Python development experience, including maintaining code running in production
• Professional experience building or supporting production data pipelines
• Experience with a modern data transformation framework; dbt strongly preferred
• Strong understanding of dimensional data modeling, including:
  
  • Fact and dimension tables
  • Data grain
  • Conformed dimensions
  • Slowly changing dimensions
• Experience supporting production environments, including deployments, monitoring, environment management, troubleshooting, and incident response
• Strong experience with Git, pull requests, code reviews, and CI/CD
• Ability to independently investigate and debug unfamiliar technical problems
• Strong communication skills and ability to work across technical and business teams
• Comfortable contributing within an established architecture and engineering environment

Preferred Qualifications

• Experience with Snowflake
• Experience with Microsoft Azure
• Familiarity with Azure Data Factory (ADF), ADLS Gen2, and/or Key Vault
• Terraform or other Infrastructure-as-Code experience
• Experience working with multi-tenant or customer-facing data
• Experience integrating with or reverse-engineering legacy and third-party systems
• Familiarity with BI, analytics, and downstream data consumption patterns
• Experience working with supply chain, transportation, warehousing, logistics, or other operational data

AI & Modern Engineering
The engineering team incorporates AI-assisted tools into its development and problem-solving workflows. Candidates should be comfortable using AI tools thoughtfully to:

• Investigate unfamiliar codebases, schemas, documentation, and systems
• Research and evaluate potential technical approaches
• Automate repetitive engineering tasks and workflows
• Improve development and troubleshooting efficiency
• Build custom scripts, tools, agents, or other AI-assisted workflows

Candidates should also understand the limitations of AI-generated output and be able to explain how they verify results, identify incorrect assumptions, and validate technical solutions before putting them into production.