Capital Technology Group seeks a Lead Data Engineer to build scalable AWS-native data platforms for mission-critical analytics in federal environments. Responsibilities Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Python , Apache Spark (PySpark) , SQL (PostgreSQL) , and AWS Glue Develop and optimize AWS-native data platforms using AWS Glue , Amazon EMR , Amazon MWAA (Apache Airflow) , Amazon S3 , RDS , and CloudWatch Build high-performance ingestion, transformation, and orchestration workflows for structured and semi-structured data with Apache Iceberg and Parquet Design and optimize analytical platforms using Amazon Athena , Trino , Hive , OpenSearch , and enterprise data catalog technologies Build AI-enabled data solutions using Amazon Bedrock , RAG pipelines , and vector search technologies including Amazon S3 Vectors and OpenSearch vector indexes Develop cloud infrastructure via CloudFormation (Infrastructure as Code) and implement changes through GitHub with enterprise CI/CD pipelines Improve reliability, scalability, performance, and maintainability through monitoring, troubleshooting, automation, and continuous optimization Support mission-critical analytics and reporting in large-scale AWS-based federal data environments, implementing solutions aligned to FedRAMP and NIST SP 800-53 security controls Mentor junior engineers through technical guidance, architecture discussions, and code reviews, promoting engineering best practices Collaborate with cross-functional teams in an Agile environment to define requirements, deliver high-quality data solutions, and communicate technical concepts to technical and non-technical stakeholders Requirements Bachelor’s degree in Computer Science, Engineering, or a related technical field 15+ years of professional experience in data engineering, data architecture, or related fields Strong hands-on experience with Apache Spark (PySpark) required, plus Python , SQL (PostgreSQL) , and dbt for large-scale data engineering, ETL/ELT development, data transformation, and data modeling Experience with AWS Glue , Amazon EMR , Amazon MWAA (Apache Airflow) , AWS Lambda , Amazon S3 , Amazon RDS Experience developing scalable data pipelines, workflow orchestration, and data integration solutions across enterprise environments Experience with modern data lake technologies and formats such as Parquet and Iceberg Experience designing and optimizing solutions using relational and NoSQL databases Ability to build reliable, high-performance data platforms using performance tuning and enterprise-scale ETL/ELT architectures Strong analytical and problem-solving skills Experience working in Agile , iterative software development environments Ability to quickly learn and apply new technologies and domain knowledge Excellent written and verbal communication skills, including the ability to explain complex topics to diverse audiences Technologies Python; Apache Spark (PySpark); SQL (PostgreSQL); AWS Glue; Amazon EMR; Amazon MWAA (Apache Airflow); Amazon S3; RDS; CloudWatch Apache Iceberg; Parquet; Amazon Athena; Trino; Hive; OpenSearch; enterprise data catalog technologies Amazon Bedrock; RAG pipelines; vector search technologies; Amazon S3 Vectors; OpenSearch vector indexes CloudFormation (Infrastructure as Code); GitHub; CI/CD pipelines FedRAMP; NIST SP 800-53; AWS Lambda; dbt; NoSQL databases Benefits Remote Work (Hybrid roles will be specified in the job post) Competitive Compensation Package Medical, Dental, and Vision Life Insurance, Short/Long Term Disability Employee Assistance Program 401(k) with 4% matching Liberal PTO vacation policy Generous Annual Continuing Education Annual Wellness Budget Bonus Incentive Programs (Employee referrals and performance-based rewards) Client Requirements Applicants MUST be US Citizens and be able to obtain Public Trust clearance Location & Salary Location: Washington, DC (hybrid) Salary: USD 150,000 - 200,000 per year (estimated range; final offer may vary based on experience, skills, and other factors; range is not a guarantee and subject to change) #J-18808-Ljbffr
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