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# Data Engineer - Mid Level - Remote

- **Company:** Levi, Ray & Shoup, Inc.
- **Location:** Los Angeles, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $79,040.0 - $104,000.0
- **Contract:** Temporary to permanent
- **Skills:** Query Performance, Data Analysis, Big Data, Cloud Computing, Code Review, Computer Programming, Databases, Data Cleansing, Information Engineering, Data Files, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Data Warehousing, Relational Databases, Database Development, Database Queries, Dimensional Modeling, Python (Programming Language), Microsoft SQL Server, Performance Tuning, Systems Development Life Cycle, Software Engineering, Transact-SQL, Data Ingestion, System Availability, Snowflake, Git, Non-relational Database, Data Management, Data Pipelines
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-mid-level-remote-los-angeles-ca--780520ac-da15-4c23-9b3d-951109576f7b

## About the Role

Consulting, Data Analysis, Data Science, Data Management, Cross-Functional, Business Support, Time Management, Database Extract Transform and Load (ETL), Data Cleaning, Data Sets, * Experience: Must have 3+ years of experience in data engineering, software development, or related field.

Technical Skills:

 * Strong SQL skills (complex queries, performance tuning, relational modeling).
 * Experience developing and optimizing data solutions in Snowflake.
 * Hands-on data development experience with dbt (dbt Core and/or dbt Cloud), including modular modeling, testing, and documentation.
 * Experience with relational databases such as SQL Server (or similar enterprise RDBMS).
 * Proficiency with at least one programming/scripting language used for data engineering (e.g., T-SQL, Python, or similar).
 * Solid understanding of data warehousing concepts (dimensional modeling, staging/curation layers, incremental processing).
 * Familiarity with Git and collaborative development workflows (branching, pull requests, code reviews).
 * Understanding of SDLC and project delivery practices (requirements, design, build, test, deploy, support).

Soft Skills:

 * Excellent problem-solving and analytical skills.
 * Strong communication skills with the ability to work in cross-functional teams.
 * Ability to manage multiple tasks in a fast-paced environment.
 * Strong attention to detail and commitment to delivering high-quality work.
 * Candidate must be able to effectively communicate in English (written & verbal)
 * Candidate must have permanent authorization to work in the USA for any employer
 * Corp to corp candidates will not be considered.

The base range for this contract position is $38.00 - $50.00 per hour, depending on experience. Our pay ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hires of this position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

## Description

As a Data Engineer, you will be responsible for designing, building, and maintaining efficient and scalable data pipelines and data-centric solutions. You will collaborate closely with cross-functional IT teams, data analysts, data scientists, and other stakeholders to ensure the availability, reliability, and accessibility of data to support various business needs., * Data Pipeline Development: Design, develop, and optimize data pipelines for data ingestion, transformation, and storage.

 * ETL/ELT Processes: Build and manage Extract, Transform, Load (ETL) processes to ensure timely, clean, and accurate data for analytics and reporting.
 * Data Integration: Integrate data from various internal and external data sources into the organizations data platform.
 * Database Management: Work with relational databases (e.g., SQL Server) and potentially non-relational databases to store, retrieve, and manage large datasets.
 * Data Governance: Implement and enforce data quality, governance, and security policies to ensure compliance with regulatory and company standards.
 * Optimization: Optimize data workflows, query performance, and storage solutions for efficiency and cost-effectiveness.
 * Automation: Automate repetitive data processes and workflows to improve efficiency and reduce errors.
 * Monitoring & Troubleshooting: Implement monitoring, alerting, and troubleshooting processes for data systems to ensure high availability and performance.