SteerBridge is a modern technology company delivering innovative, mission‑focused solutions to the U.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 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.
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.
In this role, you will be responsible for performing Data Engineering tasks within the existing systems of record with multiple databases. Your mission will be to enhance and optimize data entry, management, and extraction within this database to ensure its usability within our proprietary system. Data management activities include performing data quality checks, analysis, presenting data, and documenting the process. S. Citizen.
Bachelor's Degree or Above in Systems Engineering, Computer Science, or related field.
Must hold, or be able to obtain, a Public Trust clearance (an active Secret or Top Secret clearance also satisfies this requirement).
Experience in data pipelines, utilizing advanced analytics tools and platforms and Python.
Extensive experience with major tools such as Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git; Preferred local to the Vienna, VA area and able to work on-site at our Vienna, VA office (3+ days/week). Data Modeling and Design
Advanced data modeling (conceptual, logical, and physical) with emphasis on scalability and maintainability.
Strong understanding of database paradigms (relational, NoSQL, graph, time-series, and document-based).
Expertise with modern data warehousing platforms (Redshift, Snowflake, BigQuery).
Deep understanding of dimensional modeling (star/snowflake schemas) and data vault techniques.
Proficiency with schema evolution, metadata-driven pipelines, and data versioning strategies.
Implementing data retention, archival, and lifecycle policies.
Project Experience: Delivered optimized, production-grade data models supporting analytics, reporting, and ML workflows, aligning with established architecture and performance standards.
Data Pipeline Development
Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue, GCP Dataflow, Azure Data Factory).
Proficient in pipeline automation, observability, and monitoring (metrics, logging, alerting).
Strong Python development for ETL — modular, testable, reusable, and performance-optimized.
Project Experience: Owned the end-to-end design and implementation of fault-tolerant, high-throughput pipelines integrating diverse data sources while maintaining data quality and SLAs.
Cloud Platforms and Services
Deep expertise in AWS, GCP, or Azure data ecosystems.
Experience building and managing cloud-native data solutions (Data Lakes, Data Warehouses, Data Mesh).
Strong understanding of cloud storage (S3, Blob), managed databases (RDS, DynamoDB), and compute (EMR, Dataproc, ECS).
Cost governance and performance optimization for large-scale data workloads.
Knowledge of serverless data patterns (AWS Lambda + Athena, GCF + BigQuery).
Experience with hybrid/multi-cloud architecture and inter-cloud data movement.
Project Experience: Led migration of legacy ETL workflows and data systems to cloud-native architectures, delivering measurable cost, scalability, and performance improvements.
Big Data Technologies
Proficiency with data lake and lakehouse architectures (Delta Lake, Apache Iceberg, Apache Hudi).
Understanding of partitioning, data compaction, schema evolution, and ACID compliance.
Performance tuning in petabyte-scale distributed systems.
Project Experience: Built and maintained data platforms capable of processing structured and unstructured data at scale, enabling advanced analytics and data science workloads.
Database Administration and Optimization
Advanced SQL/NoSQL query tuning, indexing, sharding, and partitioning strategies.
Proficient with replication, backups, and disaster recovery across distributed systems.
Experience optimizing data-intensive application code and database interfaces.
Familiarity with temporal tables, data versioning, and caching strategies.
Project Experience: Improved query performance and system scalability through advanced indexing, schema refactoring, and distributed database optimization.
Data Governance and Security
Implementing data privacy and compliance frameworks (GDPR, CCPA).
Experience with data cataloging, lineage, and metadata management (DataHub, Collibra, Alation).
Role-based access control and sensitive data protection across multi-tenant systems.
Integration of data quality validation and data contract testing within CI/CD pipelines.
Automation of governance and security policies using cloud-native tools.
Project Experience: Implemented enterprise-grade data governance and access control frameworks ensuring compliance, lineage visibility, and trust in analytics.
Programming and Software Engineering
Strong proficiency in Python and SQL for data processing, automation, and API integration.
Expertise in object-oriented programming (OOP) and design patterns in Python.
Deep understanding of algorithmic complexity (Big O) and code performance optimization.
Proficient in software engineering best practices: testing (pytest/unittest), documentation, type hinting, and linting.
Ability to debug, profile, and optimize large-scale data workflows.
Project Experience: Developed performant, maintainable Python-based data frameworks, automated ETL systems, and optimized code for distributed workloads.
AI/ML Pipeline Enablement
Collaboration with data scientists on feature engineering, data preparation, and model deployment.
Knowledge of ML orchestration and experiment tracking (MLflow, Kubeflow).
Familiarity with feature stores and data lineage for ML.
Integration of batch and streaming data pipelines for real-time inference.
Hands-on experience with analytics and visualization tools (Tableau, Power BI).
Project Experience: Built and maintained ML-ready data pipelines and infrastructure supporting training, experimentation, and real-time inference.
Mentoring and guiding junior engineers in data design, coding standards, and performance optimization.
Leading cross-functional projects with data scientists, analysts, and business partners.
Promoting best practices for data engineering and governance within the organization.
Effective stakeholder communication, documentation, and Agile project management.
Project Experience: Provided technical leadership for cross-domain data initiatives, fostering best practices in data engineering and enabling scalable, maintainable systems.
DevOps/DataOps:
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.
In this role, you will be responsible for performing Data Engineering tasks within the existing systems of record with multiple databases. Your mission will be to enhance and optimize data entry, management, and extraction within this database to ensure its usability within our proprietary system. Data management activities include performing data quality checks, analysis, presenting data, and documenting the process. S. Citizen.
Bachelor's Degree or Above in Systems Engineering, Computer Science, or related field.
Must hold, or be able to obtain, a Public Trust clearance (an active Secret or Top Secret clearance also satisfies this requirement).
Experience in data pipelines, utilizing advanced analytics tools and platforms and Python.
Extensive experience with major tools such as Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git; Preferred local to the Vienna, VA area and able to work on-site at our Vienna, VA office (3+ days/week). Data Modeling and Design
Advanced data modeling (conceptual, logical, and physical) with emphasis on scalability and maintainability.
Strong understanding of database paradigms (relational, NoSQL, graph, time-series, and document-based).
Expertise with modern data warehousing platforms (Redshift, Snowflake, BigQuery).
Deep understanding of dimensional modeling (star/snowflake schemas) and data vault techniques.
Proficiency with schema evolution, metadata-driven pipelines, and data versioning strategies.
Implementing data retention, archival, and lifecycle policies.
Project Experience: Delivered optimized, production-grade data models supporting analytics, reporting, and ML workflows, aligning with established architecture and performance standards.
Data Pipeline Development
Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue, GCP Dataflow, Azure Data Factory).
Proficient in pipeline automation, observability, and monitoring (metrics, logging, alerting).
Strong Python development for ETL — modular, testable, reusable, and performance-optimized.
Project Experience: Owned the end-to-end design and implementation of fault-tolerant, high-throughput pipelines integrating diverse data sources while maintaining data quality and SLAs.
Cloud Platforms and Services
Deep expertise in AWS, GCP, or Azure data ecosystems.
Experience building and managing cloud-native data solutions (Data Lakes, Data Warehouses, Data Mesh).
Strong understanding of cloud storage (S3, Blob), managed databases (RDS, DynamoDB), and compute (EMR, Dataproc, ECS).
Cost governance and performance optimization for large-scale data workloads.
Knowledge of serverless data patterns (AWS Lambda + Athena, GCF + BigQuery).
Experience with hybrid/multi-cloud architecture and inter-cloud data movement.
Project Experience: Led migration of legacy ETL workflows and data systems to cloud-native architectures, delivering measurable cost, scalability, and performance improvements.
Big Data Technologies
Proficiency with data lake and lakehouse architectures (Delta Lake, Apache Iceberg, Apache Hudi).
Understanding of partitioning, data compaction, schema evolution, and ACID compliance.
Performance tuning in petabyte-scale distributed systems.
Project Experience: Built and maintained data platforms capable of processing structured and unstructured data at scale, enabling advanced analytics and data science workloads.
Database Administration and Optimization
Advanced SQL/NoSQL query tuning, indexing, sharding, and partitioning strategies.
Proficient with replication, backups, and disaster recovery across distributed systems.
Experience optimizing data-intensive application code and database interfaces.
Familiarity with temporal tables, data versioning, and caching strategies.
Project Experience: Improved query performance and system scalability through advanced indexing, schema refactoring, and distributed database optimization.
Data Governance and Security
Implementing data privacy and compliance frameworks (GDPR, CCPA).
Experience with data cataloging, lineage, and metadata management (DataHub, Collibra, Alation).
Role-based access control and sensitive data protection across multi-tenant systems.
Integration of data quality validation and data contract testing within CI/CD pipelines.
Automation of governance and security policies using cloud-native tools.
Project Experience: Implemented enterprise-grade data governance and access control frameworks ensuring compliance, lineage visibility, and trust in analytics.
Programming and Software Engineering
Strong proficiency in Python and SQL for data processing, automation, and API integration.
Expertise in object-oriented programming (OOP) and design patterns in Python.
Deep understanding of algorithmic complexity (Big O) and code performance optimization.
Proficient in software engineering best practices: testing (pytest/unittest), documentation, type hinting, and linting.
Ability to debug, profile, and optimize large-scale data workflows.
Project Experience: Developed performant, maintainable Python-based data frameworks, automated ETL systems, and optimized code for distributed workloads.
AI/ML Pipeline Enablement
Collaboration with data scientists on feature engineering, data preparation, and model deployment.
Knowledge of ML orchestration and experiment tracking (MLflow, Kubeflow).
Familiarity with feature stores and data lineage for ML.
Integration of batch and streaming data pipelines for real-time inference.
Hands-on experience with analytics and visualization tools (Tableau, Power BI).
Project Experience: Built and maintained ML-ready data pipelines and infrastructure supporting training, experimentation, and real-time inference.
Mentoring and guiding junior engineers in data design, coding standards, and performance optimization.
Leading cross-functional projects with data scientists, analysts, and business partners.
Promoting best practices for data engineering and governance within the organization.
Effective stakeholder communication, documentation, and Agile project management.
Project Experience: Provided technical leadership for cross-domain data initiatives, fostering best practices in data engineering and enabling scalable, maintainable systems.
DevOps/DataOps:
Senior Data Engineer (Vienna) in vienna at Unknown Company
This position is listed as contract and hybrid.