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.
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