Data Engineer

About this role

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 Data Engineer to design, build, and maintain scalable, efficient data pipelines and systems following modern data engineering best practices. The Data Engineer will partner with other Data Engineers to evaluate and prototype new tools and technologies, assessing their risks and benefits to 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), 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.

Who You Are

• A strategic data engineer who enjoys designing complex systems and solving complex challenges

• Strong 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

• 4+ years of professional experience in data engineering or related domains

• Strong hands-on experience with:

• Databricks , Apache Spark (PySpark) , Python , SQL (PostgreSQL) , and dbt for large-scale data engineering, ETL/ELT development, data transformation, and data modeling.

• Designing, building, and maintaining AWS-native data platforms using 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 including Apache Iceberg 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 tuning , system optimization , and enterprise-scale ETL/ELT architectures.

• Java development and modern CI/CD practices using Harness .

• 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, with the ability to explain complex topics to diverse audiences

Nice to Have

• Experience supporting analytics, data engineering, or modernization initiatives for financial regulators, capital markets, or other highly regulated environments is a plus.

• Experience with Kafka (streaming/data pipelines)

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