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# TS4 VISION ETL Lead

- **Company:** DKMRBH Inc.
- **Location:** Des Moines, IA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $124,800.0 - $128,960.0
- **Contract:** Temporary to permanent
- **Skills:** Data Analysis, BigQuery, Code Review, Databases, Data Architecture, Data Validation, Information Engineering, Data Integration, Data Integrity, Extract Transform Load (ETL), Data Transformation, Data Migration, Data Warehousing, Relational Databases, Database Testing, Cloud Services, SQL Databases, Strategies of Testing, Enterprise Data Management, Data Processing, Google Cloud, Enterprise Software Applications, Data Ingestion, IT Architecture, Data Lakes, Google Bigquery, Software Coding, Data Pipelines
- **Published:** September 26, 2026
- **Apply:** https://www.careerjet.com/jobad/us40796cb252d8d98db0fd2751d3c00bd9

## About the Role

7+ years of hands-on ETL development experience.
7+ years of experience with modern ETL and reporting/BI technologies.
7+ years of experience with enterprise data models and ETL pipeline design best practices.
7+ years of experience providing technical leadership to ETL/Data Engineering teams, including teams of 4 or more developers.
Strong hands-on experience with Google Cloud Dataform.
Strong hands-on experience with Google BigQuery.
Strong experience with Medallion Architecture.
Experience designing and developing enterprise data ingestion and migration pipelines.
Strong understanding of data warehousing, data lakes, analytical databases, and enterprise data models.
Strong understanding of data quality, validation, testing, and reconciliation.
Excellent communication and presentation skills.
Ability to communicate effectively with technical teams, business stakeholders, and leadership. Technical Skills
ETL & Data Engineering: ETL, ELT, Data Integration, Data Ingestion, Data Migration, Data Pipelines, Data Transformation, Data Warehousing, Data Lakes
Google Cloud: Google Cloud Platform (GCP), BigQuery, Google Cloud Dataform
Data Architecture: Medallion Architecture, Enterprise Data Models, Data Modeling, Data Warehouse Architecture, Analytical Databases
Data Quality: Data Validation, Data Integrity, Data Reconciliation, Data Testing, Data Quality
Leadership: Technical Leadership, Team Leadership, Mentoring, ETL Development Standards, Code Reviews, Architecture Guidance
Source Systems: Relational Databases, SQL, Flat Files, Enterprise Applications Preferred Profile

## Description

We are seeking a highly skilled Lead ETL Developer / Data Engineering Lead to design, develop, test, and deploy enterprise data ingestion and migration pipelines.
The ideal candidate will have strong hands-on experience with ETL development, cloud data platforms, data modeling, data warehouse migration, Google Cloud technologies, BigQuery, Dataform, and Medallion Architecture. This position will also provide technical leadership, mentoring, architecture guidance, and development standards for an ETL engineering team. Key Responsibilities
Lead the development of ETL and data ingestion pipelines supporting enterprise data migration and modernization initiatives.
Design and implement scalable pipelines using modern ETL tools and cloud-native data processing technologies.
Extract data from disparate sources including relational databases, flat files, cloud data platforms, and other enterprise systems.
Transform, standardize, validate, and load data into SQL data warehouses, data lakes, and analytical databases.
Develop data pipelines using Google Cloud Dataform and BigQuery.
Apply Medallion Architecture principles to data processing and pipeline design.
Lead and mentor a team of 4+ ETL/Data Developers, providing technical guidance, coding standards, and development best practices.
Collaborate with business and technical stakeholders to understand data requirements and downstream reporting and analytics needs.
Validate transactional and analytical data models to ensure required data elements are available for downstream applications and BI solutions.
Develop and execute testing strategies to ensure data integrity, completeness, accuracy, and reliability.
Troubleshoot data pipeline and integration issues and implement appropriate solutions.
Optimize ETL pipelines for scalability, performance, reliability, and maintainability.
Establish consistent ETL development patterns, standards, and best practices.
Prepare technical documentation, presentations, and status updates for technical and non-technical stakeholders.