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# Data Engineer

- **Company:** Gainwell Technologies
- **Location:** Dallas, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $69,400.0 - $99,200.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Data Analysis, Business Process Modeling, Cloud Computing, Software Quality, Code Review, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Data Visualization, Python (Programming Language), Performance Tuning, Power BI, Software Construction, SQL Databases, Tableau (Software), Workflow Management Systems, Data Processing, Apache Spark, Data Lakes, Looker Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** June 28, 2026
- **Apply:** https://www.dice.com/job-detail/e70f6fd6-c8cc-499d-9c01-d320654e8561

## About the Role

* 3+ years of experience as a Data Engineer or in a similar role.
 * Strong hands-on experience with Databricks (Spark, Delta Lake) and Python-based ETL frameworks.
 * Solid experience working with AWS cloud services for data processing and storage.
 * Moderate-to-advanced proficiency in SQL for data wrangling, transformation, and performance tuning.
 * Experience with data lake architectures, ELT/ETL development, and orchestration tools.
 * Familiarity with software engineering best practices, including CI/CD, version control, and code reviews.
 * Experience with Power BI or other BI tools (e.g., Tableau, Looker) to assist in data visualization or self-service reporting enablement.

## Description

The Senior Data Engineer will be responsable on designing, developing, and maintaining scalable data pipelines and models that power analytics and decision?making across the organization. It blends hands?on engineering with cross?functional collaboration, requiring expertise in Databricks, Spark, Python, AWS, and modern data architecture. The role emphasizes performance optimization, automation, data governance, and the ability to independently deliver end?to?end data solutions., * Architect Scalable Data Pipelines: Design, develop, and maintain reliable ETL/ELT workflows using Databricks, Spark, and Python.

 * Enable Data Access & Analytics: Partner with analytics, product, and engineering teams to ensure timely, accurate, and governed access to data for downstream reporting and analytics.
 * Optimize Data Workflows: Improve performance, reduce latency, and streamline processes by tuning SQL, optimizing Spark jobs, and enhancing cloud data pipelines.
 * Leverage Cloud Infrastructure: Utilize AWS services (e.g., S3, Glue, Lambda) to manage and scale data engineering workloads.
 * Drive Best Practices: Establish and maintain data engineering standards, including code quality, data security, version control, and documentation.
 * Build & Maintain Data Models: Construct and support dimensional and normalized data models that support cross-functional use cases and reporting needs.
 * Automation & Monitoring: Set up robust pipeline orchestration (e.g., with Airflow, Databricks Jobs, or AWS Step Functions) and monitoring/alerting systems.
 * Collaborate Cross-Functionally: Work with data analysts, scientists, and business users to understand requirements and transform raw data into business-ready datasets.


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