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. 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. The ideal candidate is a quick learner, curious, innovative, results-oriented, and has strong interpersonal skills. Required Must be a U.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). Minimum 6+ 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. Location: Preferred local to the Vienna, VA area and able to work on-site at our Vienna, VA office (3+ days/week). Hybrid opportunities at supervisors' discretion. Experience 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