Pay: USD 135,000–160,000/year — pay source
Salary 135,000 – 160,000 USD per year SteerBridge is proud to be an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace where all qualified applicants and employees are treated with respect and dignity—regardless of race, color, gender, age, religion, national origin, ancestry, disability, veteran status, genetic information, sexual orientation, or any other characteristic protected by law.
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Work setup: On-site stated — work setup source
Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn. Location: Preference for candidates local to the Vienna, VA area who are able to work on-site at our Vienna office three or more days per week. Hybrid opportunities may be available at the supervisor’s discretion. Preferred Qualifications
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Employment: Full-time — employment source
Employment type Full-time
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What you’ll work on

Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies.
Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments.

From the employer’s posting

Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies. Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies. Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments.

Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies. Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments. Develop maintainable, production-quality Python and SQL solutions using software engineering best practices, including version control, testing, CI/CD, documentation, performance profiling, and reusable design patterns.

What you’ll bring

All qualifications

Core experience

Bachelor’s degree or higher in Systems Engineering, Computer Science, or a related field.
6+ years of relevant professional experience, including experience developing and supporting data pipelines using advanced analytics tools, platforms, and Python.
Experience scripting, developing tools, and automating large-scale computing environments.
Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn.

Preferred experience

Experience with DevOps/DataOps practices, including infrastructure as code, Docker, Kubernetes, and automated deployment of data infrastructure.
Experience with Git-based workflows, automated integration testing, and CI/CD for data pipelines.
Experience optimizing query performance, pipeline efficiency, and cloud or compute resource utilization.
Experience developing resilient, automated data pipelines with retry logic, monitoring, alerting, and self-healing capabilities.

Qualification wording

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

6+ years of relevant professional experience, including experience developing and supporting data pipelines using advanced analytics tools, platforms, and Python.

Experience scripting, developing tools, and automating large-scale computing environments.

Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn.

Experience with DevOps/DataOps practices, including infrastructure as code, Docker, Kubernetes, and automated deployment of data infrastructure.

Experience with Git-based workflows, automated integration testing, and CI/CD for data pipelines.

Experience optimizing query performance, pipeline efficiency, and cloud or compute resource utilization.

Experience developing resilient, automated data pipelines with retry logic, monitoring, alerting, and self-healing capabilities.

Tools in this posting

Python
SQL
AWS
Azure
BigQuery
Delta
Docker
Google Cloud (GCP)
Iceberg
Kafka
Kubernetes
NoSQL
Redshift
Snowflake
Spark
Airflow
NumPy
pandas
PySpark
PyTorch
Scipy
TensorFlow
scikit-learn

Source

- Design and maintain scalable conceptual, logical, and physical data models supporting analytics, reporting, machine learning, and transactional workloads across relational, NoSQL, graph, time-series, and document-based systems. - Design, build, and optimize fault-tolerant batch and real-time data pipelines using Python and distributed processing and orchestration technologies such as Kafka, Airflow, Spark, Flink, and NiFi. - Architect and manage cloud-native data solutions across AWS, GCP, or Azure, including data lakes, data warehouses, lakehouse architectures, and hybrid or multi-cloud environments.

- Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments. - Develop maintainable, production-quality Python and SQL solutions using software engineering best practices, including version control, testing, CI/CD, documentation, performance profiling, and reusable design patterns. - Partner with data scientists, analysts, engineers, and business stakeholders to support ML-ready data pipelines and analytics solutions while providing technical leadership, mentoring engineers, and establishing data engineering and architecture standards.

- Bachelor’s degree or higher in Systems Engineering, Computer Science, or a related field. - 6+ years of relevant professional experience, including experience developing and supporting data pipelines using advanced analytics tools, platforms, and Python. - Experience scripting, developing tools, and automating large-scale computing environments.

- Experience scripting, developing tools, and automating large-scale computing environments. - Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn. - Location: Preference for candidates local to the Vienna, VA area who are able to work on-site at our Vienna office three or more days per week. Hybrid opportunities may be available at the supervisor’s discretion.

- Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies. - Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies. - Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments.

- Design, build, and optimize fault-tolerant batch and real-time data pipelines using Python and distributed processing and orchestration technologies such as Kafka, Airflow, Spark, Flink, and NiFi. - Architect and manage cloud-native data solutions across AWS, GCP, or Azure, including data lakes, data warehouses, lakehouse architectures, and hybrid or multi-cloud environments. - Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies.

- Architect and manage cloud-native data solutions across AWS, GCP, or Azure, including data lakes, data warehouses, lakehouse architectures, and hybrid or multi-cloud environments. - Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies. - Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies.

Preferred Qualifications - Experience with DevOps/DataOps practices, including infrastructure as code, Docker, Kubernetes, and automated deployment of data infrastructure. - Experience with Git-based workflows, automated integration testing, and CI/CD for data pipelines.

Key Responsibilities - Design and maintain scalable conceptual, logical, and physical data models supporting analytics, reporting, machine learning, and transactional workloads across relational, NoSQL, graph, time-series, and document-based systems. - Design, build, and optimize fault-tolerant batch and real-time data pipelines using Python and distributed processing and orchestration technologies such as Kafka, Airflow, Spark, Flink, and NiFi.

Benefits in the posting

Full benefits wording

Health insurance
Dental insurance
Vision insurance
Life Insurance
401(k) Retirement Plan with matching
Paid Time Off
Paid Federal Holidays
SteerBridge also offers benefits that may include health and life insurance, paid time off, paid holidays, disability coverage, retirement savings, and professional development opportunities. Benefits vary by position and eligibility.

From the employer’s posting.

Job description

View original posting ↗

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 AI/ML Maintenance, Sustainment, and Deployment Planning Project. Our team is dedicated to harnessing the power of AI/ML to increase parts availability and reduce maintenance wait times, ultimately maximizing aircraft availability.

In this role, you will be responsible 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. The ideal candidate is a quick learner, curious, innovative, results-oriented and has strong interpersonal skills

Key Responsibilities

Design and maintain scalable conceptual, logical, and physical data models supporting analytics, reporting, machine learning, and transactional workloads across relational, NoSQL, graph, time-series, and document-based systems.
Design, build, and optimize fault-tolerant batch and real-time data pipelines using Python and distributed processing and orchestration technologies such as Kafka, Airflow, Spark, Flink, and NiFi.
Architect and manage cloud-native data solutions across AWS, GCP, or Azure, including data lakes, data warehouses, lakehouse architectures, and hybrid or multi-cloud environments.
Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies.
Optimize SQL and NoSQL databases through query tuning, indexing, sharding, partitioning, replication, caching, backup, and disaster recovery strategies.
Implement data governance, quality, lineage, metadata management, access controls, lifecycle policies, and security standards to support reliable and compliant data environments.
Develop maintainable, production-quality Python and SQL solutions using software engineering best practices, including version control, testing, CI/CD, documentation, performance profiling, and reusable design patterns.
Partner with data scientists, analysts, engineers, and business stakeholders to support ML-ready data pipelines and analytics solutions while providing technical leadership, mentoring engineers, and establishing data engineering and architecture standards.

Required Qualifications

Eligibility requirements: U.S. citizenship is required for this position under applicable federal contract requirements. Candidates must also be able to obtain and maintain the security clearance required for the role.
Bachelor’s degree or higher in Systems Engineering, Computer Science, or a related field.
6+ years of relevant professional experience, including experience developing and supporting data pipelines using advanced analytics tools, platforms, and Python.
Experience scripting, developing tools, and automating large-scale computing environments.
Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn.
Location: Preference for candidates local to the Vienna, VA area who are able to work on-site at our Vienna office three or more days per week. Hybrid opportunities may be available at the supervisor’s discretion.

Preferred Qualifications

Experience with DevOps/DataOps practices, including infrastructure as code, Docker, Kubernetes, and automated deployment of data infrastructure.
Experience with Git-based workflows, automated integration testing, and CI/CD for data pipelines.
Experience optimizing query performance, pipeline efficiency, and cloud or compute resource utilization.
Experience developing resilient, automated data pipelines with retry logic, monitoring, alerting, and self-healing capabilities.
Ability to clearly document and communicate technical architectures using tools such as Lucidchart, PlantUML, or Draw.io.

Benefits

Health insurance
Dental insurance
Vision insurance
Life Insurance
401(k) Retirement Plan with matching
Paid Time Off
Paid Federal Holidays

The posted compensation range reflects the scope and requirements of this position. A final offer will be determined based on the candidate’s relevant experience, skills, education, certifications, and work location, along with the responsibilities of the position. Where applicable, contract requirements, wage determinations, and federal contract labor categories may also inform the offer.

SteerBridge also offers benefits that may include health and life insurance, paid time off, paid holidays, disability coverage, retirement savings, and professional development opportunities. Benefits vary by position and eligibility.

Salary

135,000 – 160,000 USD per year

SteerBridge is proud to be an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace where all qualified applicants and employees are treated with respect and dignity—regardless of race, color, gender, age, religion, national origin, ancestry, disability, veteran status, genetic information, sexual orientation, or any other characteristic protected by law.

We also provide reasonable accommodations for individuals with disabilities in accordance with applicable laws. If you require assistance during the application process, we encourage you to reach out so we can support your needs.

Employment type

Full-time

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Source & posting history

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Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay: Salary 135,000 – 160,000 USD per year SteerBridge is proud to be an Equal Opportunity Employer. We are committed to creating a diverse and inclusive workplace where all qualified applicants and employees are treated with respect and dignity—regardless of race, color, gender, age, religion, national origin, ancestry, disability, veteran status, genetic information, sexual orientation, or any other characteristic protected by law.
Location & working pattern: Vienna, VA
- Design, build, and optimize fault-tolerant batch and real-time data pipelines using Python and distributed processing and orchestration technologies such as Kafka, Airflow, Spark, Flink, and NiFi. - Architect and manage cloud-native data solutions across AWS, GCP, or Azure, including data lakes, data warehouses, lakehouse architectures, and hybrid or multi-cloud environments. - Develop and optimize large-scale data platforms using technologies such as Redshift, Snowflake, BigQuery, Delta Lake, Apache Iceberg, and Apache Hudi, applying appropriate partitioning, schema evolution, and performance optimization strategies.
More source context
- Proficiency with Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git, with familiarity in TensorFlow, PyTorch, and Scikit-learn. - Location: Preference for candidates local to the Vienna, VA area who are able to work on-site at our Vienna office three or more days per week. Hybrid opportunities may be available at the supervisor’s discretion. Preferred Qualifications
Work authorization: Required Qualifications - Eligibility requirements: U.S. citizenship is required for this position under applicable federal contract requirements. Candidates must also be able to obtain and maintain the security clearance required for the role. - Bachelor’s degree or higher in Systems Engineering, Computer Science, or a related field.

Open source at jobs.lever.co ↗Read the full description ↓

Status in our records: Active
First seen by us: Jun 13, 2026
Recorded sightings: 37
Last seen by us: Oct 8, 2026

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