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# Data Cloud Engineer

- **Company:** Zachary Piper
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $120,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Amazon Web Services, Amazon S3, Microsoft Azure, Big Data, Cloud Engineering, Cyber Security, Information Systems, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Warehousing, DevOps, Apache Hive, JSON, Python (Programming Language), Operational Databases, Oracle (Applications), Role-Based Access Control, Software Engineering, SQL Databases, Unstructured Data, Data Logging, Data Ingestion, Azure Data Factory, Change Data Capture, SC Clearance, Pyspark, Information Technology, Deployment Automation, AWS Glue, AWS Data Analytics, Cloud Migration, Azure Synapse Analytics, Data Pipelines, Amazon Redshift, Databricks
- **Published:** October 8, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9226626/data-cloud-engineer

## About the Role

Active Secret Clearance.
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field.
8+ years of data engineering experience.
Strong hands-on experience with PySpark, Spark SQL, Python, and SQL.
Experience with Azure Synapse Analytics, Databricks, and/or AWS data services.
Experience building and supporting production-grade ETL/ELT pipelines.
Experience with data ingestion, integration, transformation, and CDC.
Experience troubleshooting production data environments and implementing monitoring and logging., Experience with Azure Data Factory, AWS Glue, Redshift, Amazon S3, and Oracle.
Experience supporting federal or DoD cloud modernization programs.
Familiarity with RMF, STIG, and FedRAMP.
Experience working in Agile development environments.

## Description

Design, develop, and maintain scalable cloud-based ETL/ELT pipelines using Azure and AWS technologies.
Build and optimize large-scale data transformations using PySpark and Spark SQL.
Develop data ingestion frameworks supporting structured and unstructured data, including databases, APIs, JSON, and flat files.
Implement full and incremental data loading strategies, including Change Data Capture (CDC).
Design and support enterprise data lakes, data warehouses, and analytics environments.
Implement data quality, governance, and security controls including schema validation and RBAC.
Monitor, troubleshoot, and optimize production data pipelines.
Support CI/CD implementation and automated deployments.
Partner with cloud engineering, cybersecurity, DevOps, and software development teams.