Senior Data Engineer

SteerBridge is a modern technology company delivering innovative, mission‑focused solutions to the U.S. Government and private sector.

Leveraging deep expertise in federal acquisition, digital transformation, and emerging technologies, we deliver agile, commercial‑grade capabilities that accelerate operational effectiveness and drive measurable mission success.

At the core of SteerBridge is our people—especially the veterans whose leadership, problem‑solving mindset, and commitment to excellence elevate every project we support. We don’t simply hire exceptional talent; we cultivate it

, creating meaningful career pathways for veterans, military spouses, and professionals who share our passion for advancing technology and strengthening the missions we serve.

Position Overview

SteerBridge seeks a highly skilled and motivated individual to join our team as a Senior Data Engineer to align data solutions to business requirements by planning and managing data infrastructure and strategy for our Modern Disability Claims AI/ML program. Our team is dedicated to harnessing the power of AI/ML to increase claims processing throughput and reduce adjudication wait times, ultimately improving outcomes for veterans.

Key Responsibilties

Perform data engineering activities across existing systems of record and multiple databases.

Enhance and optimize data entry, management, and extraction processes to improve data usability within proprietary systems.

Conduct data quality checks to identify inconsistencies, errors, and opportunities for improvement.

Analyze data and present findings to support business and operational needs.

Maintain accurate documentation of data processes, workflows, and methodologies.

Collaborate with team members and stakeholders to address data needs and support continuous improvement.

Required Qualifications

Eligibility requirements:

U.S. citizenship is required for this position under applicable federal contract requirements. Candidate must also be able to obtain and maintain a Public Trust clearance; an active Secret or Top Secret security clearance also satisfies this requirement.

Bachelor’s degree or higher in Systems Engineering, Computer Science, Data Science, or a related field.

6+ years of data engineering or related experience designing, developing, and supporting production data platforms and pipelines.

Strong proficiency with Python, SQL, Pandas, PySpark, NumPy, and Git, including experience developing reusable, testable, and performance-optimized data processing and automation solutions.

Experience designing conceptual, logical, and physical data models and working with relational, NoSQL, data warehouse, data lake, and lakehouse architectures.

Hands-on experience developing and orchestrating scalable batch and/or real-time data pipelines using technologies such as Apache Spark, Kafka, Airflow, NiFi, AWS Glue, Azure Data Factory, or GCP Dataflow.

Experience developing cloud-based data solutions in AWS, Azure, and/or GCP, including cloud storage, managed databases, compute services, and modern data warehousing platforms such as Redshift, Snowflake, or BigQuery.

Experience with data quality, governance, security, lineage, metadata management, performance optimization, CI/CD, and software engineering best practices for large-scale data environments.

Preference for candidates located in the Vienna, VA area who are able to work onsite at the SteerBridge Vienna office at least three days per week. Hybrid arrangements may be available at the supervisor’s discretion.

Preferred Qualifications

• Experience with distributed computing and modern data lake/lakehouse technologies such as Hadoop, Spark, Hive, Presto/Trino, Delta Lake, Apache Iceberg, or Apache Hudi.
• Experience with DevOps/DataOps practices, including Infrastructure as Code (IaC), Docker, Kubernetes, Git-based workflows, automated testing, and CI/CD for data infrastructure and pipelines.
• Experience optimizing large-scale data environments for query performance, pipeline efficiency, scalability, and cloud resource utilization and cost.
• Experience developing resilient, automated data pipelines with monitoring, alerting, retry logic, failure recovery, and other self-healing capabilities.
• Familiarity with AI/ML data pipeline technologies such as TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow, or feature stores.
• Experience mentoring junior engineers, conducting technical reviews, and clearly documenting technical architectures using tools such as Lucidchart, PlantUML, or Draw.io.