SteerBridge Strategies is a CVE-Verified Service-Disabled, Veteran-Owned Small Business (SDVOSB) delivering a broad spectrum of professional services to the U.S. Government and private sector. Backed by decades of hands-on experience in federal acquisition and procurement, we provide agile, best-in-class commercial solutions that drive mission success. Our strength lies in our people—especially the veterans whose leadership, discipline, and dedication shape everything we do. At SteerBridge, we don’t just hire talent—we empower it, creating meaningful career paths for those who have served and those who share our commitment to excellence. 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 F-35 AI/ML Maintenance, Sustainment, and Deployment Planning Project. As the most advanced fighter jet in the world, the F-35 strengthens national security, enhances global partnerships and powers economic growth. Our F-35 Project is at the forefront of applying advanced computational analytics to revolutionize supply chain management in the aerospace industry. 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 collaboration with the National Center for Manufacturing Sciences (NCMS), we are on a mission to deliver exceptional solutions that will redefine operational readiness for the F-35 program and beyond. 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 Benefits Health insurance Dental insurance Vision insurance Life Insurance 401(k) Retirement Plan with matching Paid Time Off Paid Federal Holidays Required Must be a U.S. Citizen. Bachelor’s Degree or Above in Systems Engineering, Computer Science or related field. An active security clearance or the ability to obtain one is required. Minimum 7+ years of experience to include: Experience in data pipelines, utilizing advanced analytics tools and platforms and Python. Experience in scripting, tooling, and automating large-scale computing environments. Extensive experience with major tools such as Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git; Minor experience with TensorFlow, PyTorch, and Scikit-learn. 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. Experience designing for both OLTP and OLAP workloads. 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 Hands-on experience with distributed processing tools (Apache Kafka, Airflow, Spark, Flink, NiFi). Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue, GCP Dataflow, Azure Data Factory). Deep understanding of incremental processing, idempotency, schema evolution, and backfill logic. Proficient in pipeline automation, observability, and monitoring (metrics, logging, alerting). Strong Python development for ETL — modular, testable, reusable, and performance-optimized. Knowledge of workflow dependency management, retries, and failure recovery strategies. 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 Hands-on experience with distributed computing frameworks (Hadoop, Spark, Hive, Presto). Proficiency with data lake and lakehouse architectures (Delta Lake, Apache Iceberg, Apache Hudi). Understanding of partitioning, data compaction, schema evolution, and ACID compliance. Strong knowledge of query optimization on massive datasets (Athena, Trino, Presto). 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. Skilled in analyzing query execution plans and applying cost-based optimization. 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. Familiarity with parallel and distributed computing frameworks (Spark, Dask, Ray). Skilled with version control (Git) and CI/CD tools (GitLab, Jenkins, CircleCI). 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. Leadership and Collaboration 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. Ability to conduct technical reviews and enforce design and scalability standards. Project Experience: · Provided technical leadership for cross-domain data initiatives, fostering best practices in data engineering and enabling scalable, maintainable systems. Additional Skills DevOps/DataOps: Infrastructure as code, Docker/Kubernetes, automated deployment of data infrastructure. Testing
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