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# Data Engineer

- **Company:** Accenture
- **Location:** Denver, CO, United States (Remote available)
- **Experience:** Expert
- **Salary:** $59,100.0 - $189,300.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Data Architecture, Information Engineering, Data Integrity, Extract Transform Load (ETL), Data Systems, Python (Programming Language), SQL Databases, Data Processing, Data Management, Data Pipelines, Data Generation
- **Published:** June 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=963496095da47835

## About the Role

Do you have experience in Technical Proficiency?, Do you have a High school diploma or GED?, * Minimum 5 years of experience with advanced proficiency in Data Engineering, along with expert proficiency in Data Pipelines and Data Processing

 * Advance proficiency in Python, SQL, Data Pipelines and Data Processing
 * High School Diploma or GED

## Description

As a Data Engineer, you will be responsible for designing, developing, and maintaining data solutions that support data generation, collection, and processing. Your day-to-day activities will include creating data pipelines, ensuring data quality, and implementing extract, transform, and load processes to migrate and deploy data across various systems. You are expected to be a subject matter expert, collaborate and manage the team to perform effectively, take responsibility for team decisions, engage with multiple teams, and contribute to key decisions. Providing solutions to problems that impact multiple teams is a critical part of this role.

 * Lead the design and optimization of scalable data architectures to support evolving business needs.
 * Collaborate closely with cross-functional teams to align data solutions with organizational goals and technical standards.
 * Provide technical guidance and support to colleagues, fostering knowledge sharing and continuous improvement.
 * Analyze complex data challenges and develop innovative approaches to enhance data reliability and efficiency.
 * Drive the adoption of best practices in data management and pipeline development to elevate overall quality.


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