49 Steerbridge Data Analyst Jobs Hiring Near You

• 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 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 or more days/week). Hybrid opportunities at supervisors' discretion. 

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 & CI/CD: Git-based workflows, automated integration testing, and continuous delivery for data pipelines.
• Performance & Cost Optimization: Tuning query execution, pipeline efficiency, and resource utilization.
• Automation: Building self-healing data pipelines with retry logic, monitoring, and alerting.
• Documentation: Strong communication of technical architecture using tools like Lucidchart, PlantUML, or Draw.io.