Data Solution Architect
On-siteMcLean, Virginia, United States
Job Summary
Design greenfield data solution stacks in cloud and on-premises environments using modern data services, technologies, and industry best practices. Lead and architect the migration of legacy data environments to ensure performance and reliability. Assess data sources, models, schemas, and workflows to define scalable enterprise architectures, while evaluating existing ETL jobs, pipelines, and BI tools for modernization strategies. Address technical inquiries concerning customization, integration, and enterprise architecture functionality. Design enterprise database, data warehouse, and lakehouse solutions using Databricks across AWS, Azure, or GCP. Support an Agile software development lifecycle through architectural guidance and collaboration with cross-functional teams. Contribute to the growth of the Data Exploitation Practice.
Required Qualifications
- Ability to hold a position of public trust with the U.S. Government
- 12+ years of experience with a Bachelor's degree or 8+ years of experience with a Master's degree
- 8-12 years of industry experience in data engineering with a passion for solving complex problems
- 8-12 years of direct experience in Data Engineering with demonstrated experience in the following:
- Designing and building data pipelines and data products on modern lakehouse platforms (e.g., Databricks, Apache Spark), including Delta Lake, distributed processing, and performance optimization
- Supporting or integrating GenAI/ML workflows (e.g., feature engineering, vector storage, RAW pipelines, or model lifecycle using tools such as MLflow)
- Operating in enterprise or regulated cloud environments, including working within platform constraints, security controls, and data governance frameworks
- Building batch and streaming data pipelines using distributed processing frameworks (e.g., Apache Spark Structured Streaming) within a lakehouse architecture
- Implementing search and retrieval patterns for analytics or AI use cases (e.g., indexing, semantic search, vector-based retrieval)
- Advanced proficiency in SQL and Python for data engineering, including query authoring, optimization, relational databases, and distributed data processing frameworks (e.g., Apache Spark)
- Strong ability to lead technical discussions with technical and non-technical stakeholders, translating business needs into actionable data solutions, communicating tradeoffs, driving alignment, and guiding decision-making
- Demonstrated ability to evaluate solution feasibility, identify risks and constraints, and make pragmatic recommendations in complex or ambiguous environments
- Experience constructing complex queries to analyze results using databases or data processing development environments
- Experience architecting enterprise database, data warehouse, and lakehouse solutions
- Experience aggregating results and compiling information for reporting from multiple datasets
- Experience working in an Agile environment
- Experience providing technical leadership to cross-functional teams of developers and data scientists building web-based interfaces, dashboards, reports, analytics, and machine learning solutions
Desired Qualifications
- Experience supporting military data programs or holding a previous or active U.S. Government security clearance
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