#203 - Senior Data Engineer

SteerBridge•Vienna, VA

14d•$124,000 - $150,000•Hybrid

About The Position

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

Requirements

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 6+ years of experience.
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.
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.
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.
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.
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.
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.
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.
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.
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).

Nice To Haves

Infrastructure as code
Docker/Kubernetes
Automated deployment of data infrastructure
Git-based workflows
Automated integration testing
Continuous delivery for data pipelines
Tuning query execution, pipeline efficiency, and resource utilization
Building self-healing data pipelines with retry logic, monitoring, and alerting
Strong communication of technical architecture using tools like Lucidchart, PlantUML, or Draw.io.

Responsibilities

Performing Data Engineering tasks within existing systems of record with multiple databases.
Enhancing and optimizing data entry, management, and extraction within databases to ensure usability within our proprietary system.
Performing data quality checks, analysis, presenting data, and documenting the process.
Aligning data solutions to business requirements by planning and managing data infrastructure and strategy for the AI/ML Maintenance, Sustainment, and Deployment Planning Project.
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.
Communicating effectively with stakeholders, documenting technical architecture, and managing projects using Agile methodologies.
Conducting technical reviews and enforcing design and scalability standards.
Collaborating with data scientists on feature engineering, data preparation, and model deployment.
Building and maintaining ML-ready data pipelines and infrastructure supporting training, experimentation, and real-time inference.
Implementing data privacy and compliance frameworks (GDPR, CCPA).
Implementing enterprise-grade data governance and access control frameworks ensuring compliance, lineage visibility, and trust in analytics.
Developing performant, maintainable Python-based data frameworks, automated ETL systems, and optimized code for distributed workloads.
Leading migration of legacy ETL workflows and data systems to cloud-native architectures, delivering measurable cost, scalability, and performance improvements.
Owning the end-to-end design and implementation of fault-tolerant, high-throughput pipelines integrating diverse data sources while maintaining data quality and SLAs.
Delivering optimized, production-grade data models supporting analytics, reporting, and ML workflows, aligning with established architecture and performance standards.
Improving query performance and system scalability through advanced indexing, schema refactoring, and distributed database optimization.
Building and maintaining data platforms capable of processing structured and unstructured data at scale, enabling advanced analytics and data science workloads.
Implementing data retention, archival, and lifecycle policies.