• CTG is seeking a Senior Data Engineer to design, build, and maintain scalable, efficient data pipelines and systems following modern data engineering best practices. • Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Python, Apache Spark (PySpark), Databricks, dbt, SQL (PostgreSQL), and AWS Glue. • Develop and optimize AWS-native data platforms leveraging AWS Glue, Amazon EMR, Amazon MWAA (Apache Airflow), Lambda, Step Functions, Amazon S3, Redshift, RDS, DMS, and CloudWatch. • Build high-performance ingestion, transformation, and orchestration workflows for structured and semi-structured data using Apache Iceberg, Parquet, ORC, and Avro. • Design and optimize analytical data platforms using Amazon Athena, Trino, Hive, OpenSearch, and enterprise data catalog technologies. • Integrate enterprise and external data sources across relational and NoSQL platforms including PostgreSQL, Oracle, Redshift, GraphDB, and other NoSQL databases. • Build AI-enabled data solutions using Amazon Bedrock, RAG pipelines, and vector search technologies including Amazon S3 Vector and OpenSearch vector indexes. • Develop cloud infrastructure using CloudFormation (Infrastructure as Code), GitHub, Harness, and enterprise CI/CD pipelines while leveraging SNS, SQS, and EventBridge for event-driven architectures. • Improve the reliability, scalability, performance, and maintainability of enterprise data platforms through monitoring, troubleshooting, automation, and continuous optimization. • Support mission-critical analytics and reporting solutions within large-scale AWS-based federal data environments, implementing solutions that comply with FedRAMP and NIST 800-53 security controls. • Lead modernization initiatives migrating legacy platforms including IBM DataStage, Hadoop, RunDeck, and shell-based workflows to cloud-native AWS services. • Mentor junior engineers through technical guidance, architecture discussions, and code reviews while 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 effectively to technical and non-technical stakeholders.

• Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Databricks, dbt, Apache Spark (PySpark), SQL (PostgreSQL), and Python. • Develop and optimize AWS-native data solutions leveraging services including AWS Glue, Amazon EMR, Amazon MWAA (Apache Airflow), Lambda, Step Functions, Amazon S3, Redshift, RDS, DMS, and CloudWatch. • Build high-performance data ingestion, transformation, and orchestration workflows across structured and semi-structured data using Parquet, ORC, Avro, and Apache Iceberg. • Integrate data from enterprise and external sources, including relational and NoSQL databases such as PostgreSQL, Oracle, Redshift, GraphDB, and other NoSQL platforms. • Improve the reliability, scalability, performance, and maintainability of enterprise data platforms through monitoring, troubleshooting, and continuous optimization. • Develop and maintain automated deployment pipelines using Harness and collaborate on cloud infrastructure and platform improvements. • Support data engineering efforts powering mission-critical analytics, reporting, and decision-making across large-scale federal data environments. • Collaborate with cross-functional teams in an Agile environment to define requirements, deliver high-quality data solutions, and continuously improve engineering processes.

• Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Python, Apache Spark (PySpark), Databricks, dbt, SQL (PostgreSQL), and AWS Glue. • Develop and optimize AWS-native data platforms leveraging AWS Glue, Amazon EMR, Amazon MWAA (Apache Airflow), Lambda, Step Functions, Amazon S3, Redshift, RDS, DMS, and CloudWatch. • Build high-performance ingestion, transformation, and orchestration workflows for structured and semi-structured data using Apache Iceberg, Parquet, ORC, and Avro. • Design and optimize analytical data platforms using Amazon Athena, Trino, Hive, OpenSearch, and enterprise data catalog technologies. • Integrate enterprise and external data sources across relational and NoSQL platforms including PostgreSQL, Oracle, Redshift, GraphDB, and other NoSQL databases. • Build AI-enabled data solutions using Amazon Bedrock, RAG pipelines, and vector search technologies including Amazon S3 Vector and OpenSearch vector indexes. • Develop cloud infrastructure using CloudFormation (Infrastructure as Code), GitHub, Harness, and enterprise CI/CD pipelines while leveraging SNS, SQS, and EventBridge for event-driven architectures. • Improve the reliability, scalability, performance, and maintainability of enterprise data platforms through monitoring, troubleshooting, automation, and continuous optimization. • Support mission-critical analytics and reporting solutions within large-scale AWS-based federal data environments, implementing solutions that comply with FedRAMP and NIST 800-53 security controls. • Lead modernization initiatives migrating legacy platforms including IBM DataStage, Hadoop, RunDeck, and shell-based workflows to cloud-native AWS services. • Mentor junior engineers through technical guidance, architecture discussions, and code reviews while 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 effectively to technical and non-technical stakeholders.

• Lead the technical direction for how data flows, scales, and powers decisions at JobGet • Own the data architecture behind the platform • Drive architectural decisions • Champion modern data stack adoption • Ensure platform reliability • Build production-grade pipelines • Establish data modeling standards • Solve complex data integration and performance challenges • Architect and evolve streaming data infrastructure • Enable machine learning at scale • Establish data governance standards • Implement data validation frameworks • Raise the technical bar through mentoring