Senior Data Engineer

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.

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.