Lead Data Engineer

Capital Technology Group provides expert consulting services software development, digital transformation, human-centered design, data analytics and visualization, and cybersecurity.
Our multidisciplinary teams use agile methodologies to rapidly and incrementally deliver value in close collaboration with our clients. For over a decade, we have been trusted by both federal and commercial clients to solve complex, mission-critical business challenges. The quality of our work has been recognized by our partners and peers through our inclusion in the Digital Services Coalition, a group of forward- thinking firms recognized for excellence in delivering IT services.
Client Requirements: applicants MUST BE US Citizens and be able to obtain Public Trust clearance
The CTG Experience
At Capital Technology Group (CTG), our teams are passionate about modernizing how the federal government delivers software. We partner with federal agencies to build secure, scalable, and mission-driven solutions that make a meaningful impact on millions of people. Recognized by The Washington Post as a Top Workplace in 2025 and 2026. CTG fosters a culture rooted in our core values. Our values guide how we work together and support one another, creating an environment where employees feel trusted, empowered, and encouraged to grow both personally and professionally.
About the Role
CTG is seeking a Lead Data Engineer to design, build, and maintain scalable, efficient data pipelines and systems following modern data engineering best practices. The Lead Data Engineer will partner with other Data Engineers to evaluate and prototype new tools and technologies, assess associated risks and benefits, and deliver exceptional value to our clients.
You Will Get To
Design, build, and maintain scalable data pipelines, ETL/ELT workflows, and data models using Python, Apache Spark (PySpark), 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 Vectors 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 SP 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.
Who You Are
A strategic data engineer who enjoys designing sophisticated systems and solving challenging problems
Strong experience in modern cloud-based solution design
Comfortable balancing business needs with technical constraints and long-term strategy
A strong communicator
Collaborative, proactive, and comfortable navigating ambiguity
Qualifications
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, Python, SQL (PostgreSQL), and dbt for large-scale data engineering, ETL/ELT development, data transformation, and data modeling.
AWS Glue, Amazon EMR, Amazon MWAA (Apache Airflow), AWS Lambda, AWS Step Functions, Amazon S3, Amazon Redshift, Amazon RDS, AWS DMS, and Amazon CloudWatch.
Developing scalable data pipelines, workflow orchestration, and data integration solutions across enterprise environments.
Working with modern data lake technologies and data formats such as Parquet, ORC, and Avro.
Designing and optimizing solutions using relational and NoSQL databases including PostgreSQL, Redshift, Oracle, GraphDB, and other NoSQL platforms.
Building reliable, high-performance data platforms through performance tu