Data Engineer - CLEARED

Accountabilities:: Design secure, scalable, and high-performance database architectures supporting application modernization and cloud migration. Define data models, schemas, storage strategies, and unified data structures aligned with cloud, application, and analytics requirements. Design and implement enterprise data lakes, lakehouse architectures, and Bronze, Silver, and Gold Medallion data layers. Develop and maintain scalable ETL/ELT pipelines using AWS Glue, Apache Spark/PySpark, SQL, and Python. Implement Change Data Capture using AWS DMS to synchronize operational databases with analytical platforms such as Amazon Redshift. Develop data ingestion frameworks supporting functionality-specific data capture and enterprise application requirements. Optimize SQL queries, Spark jobs, database configurations, partitioning, indexing, and Redshift performance for large-scale datasets. Establish processes for data lineage, data quality validation, schema evolution, data versioning, and metadata management. Support event-driven architectures and application/database synchronization using AWS services. Ensure data architectures comply with applicable governance, security, privacy, and retention requirements. Document database architectures, configurations, data models, integrations, and operational procedures. Collaborate with developers, architects, infrastructure teams, business stakeholders, and Agile/Scrum teams to translate requirements into reliable enterprise data solutions. Requirements: Active Top Secret clearance required on Day 1, with the ability to complete any applicable adjudication or transfer requirements. Security+ certification required on Day 1; candidates without the certification will not be considered. U.S. citizenship required due to federal contract requirements. 7+ years of experience designing, building, and supporting enterprise data engineering solutions and modern data platforms. Strong expertise with relational databases such as PostgreSQL, Oracle, and SQL Server, along with semi-structured and NoSQL sources including DynamoDB, JSON, and APIs. Extensive experience developing dimensional data models for analytics and operational reporting. Hands-on experience with Amazon Redshift and large-scale analytical data environments. Strong hands-on experience building ETL/ELT pipelines with AWS Glue, Apache Spark/PySpark, SQL, and Python. Experience implementing Change Data Capture using AWS DMS and integrating operational databases with analytical platforms. Strong knowledge of AWS data and integration services, including S3, Glue, Redshift, Lambda, EventBridge, SQS/SNS, IAM, CloudWatch, and Secrets Manager. Experience developing metadata-driven ingestion frameworks, data lineage processes, and data quality validation solutions. Proficiency in data modeling, schema evolution, data versioning, and master/reference data management. Strong understanding of event-driven architectures and application/database synchronization patterns. Experience optimizing SQL queries, Spark jobs, partitioning strategies, indexing, and Redshift performance for large-scale datasets. Experience working in Agile/Scrum environments and collaborating effectively with application developers, architects, and business stakeholders. Excellent written and verbal communication skills. AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect certification is a plus. Experience with enterprise data lakes, lakehouse architectures, metadata repositories, data catalogs, Qlik, AWS Glue Data Catalog, or real-time and near-real-time data integration is preferred. Benefits: W2 hourly compensation of $65–$75, depending on experience, qualifications, location, and certifications. Potential benefits may include paid time off (PTO). Medical, dental, and vision coverage may be available depending on compensation type. Life insurance and long-term disability insurance may be available. 401(k) plan and additional optional benefits may be available. Fully remote work environment. Opportunity to contribute to a multi-year federal modernization and cloud migration initiative. Exposure to modern AWS data platforms, enterprise data architecture, analytics, and large-scale data engineering. Professional development opportunities through work on complex data engineering and cloud technologies. Referral program opportunities may be available.