Mid-Senior Data Engineer - SQL, Python, dbt & Snowflake Motion Recruitment - 3.7 City of Industry, CA Job Details Full-time 1 day ago Qualifications Performance monitoring Data model design Commercial use (data warehousing systems) Incident management Continuous Delivery (CD) implementation Git Research Schema design SQL Incident management operations support Production monitoring Technical troubleshooting support DevOps automation Query management Cloud monitoring Python Debugging Cross-functional communication Providing code feedback Production troubleshooting Dimensional modeling Full Job Description 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.