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# Data Engineer

- **Company:** Insight Global
- **Location:** Hollywood, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Automation of Tests, Information Systems, Continuous Integration, Information Engineering, Extract Transform Load (ETL), DevOps, Information Lifecycle Management, Python (Programming Language), Oracle (Applications), Cloud Services, SQL Databases, Enterprise Data Management, Azure Data Factory, Apache Spark, Git, Information Technology, Cloud Migration, Data Pipelines, Databricks
- **Published:** September 20, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3397728820&tx=CT2927TTZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

5+ years of experience in Data Engineering, Data Quality Engineering, or related roles.

Strong hands-on expertise with SQL, Python, Databricks, Spark, Azure Data Factory (ADF), Oracle, and cloud data platforms.

Experience building and supporting enterprise data quality frameworks, controls, monitoring, and validation processes.

Experience developing and optimizing ETL/ELT pipelines and embedding data quality checks into data workflows.

Knowledge of Git, CI/CD pipelines, automated testing, and DevOps best practices.

Strong analytical, troubleshooting, and root cause analysis skills.

Ability to collaborate with technical and business stakeholders and communicate findings effectively.

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.

## Description

Design, build, and maintain enterprise data quality frameworks, monitoring solutions, and automated validation processes to ensure trusted and reliable data across the organization. Develop and optimize ETL/ELT pipelines using Databricks, Spark, Python, SQL, and ADF while embedding data quality controls throughout the data lifecycle. Partner closely with Data Engineering, Governance, Analytics, and business teams to establish data quality standards, investigate and resolve data issues through root cause analysis, and deliver scalable technical solutions. Support CI/CD deployment processes, automated testing frameworks, cloud modernization initiatives, and present data quality findings and recommendations to both technical and business stakeholders.


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