We are looking for a seasoned Data Solution Architect to work with our team and our clients to develop enterprise grade data platforms, services, pipelines, data models, visualizations, and more! The Data Solution Architect needs to be a technologist with excellent communication and customer service skills and a passion for data and problem solving. This role spans the spectrum of data capabilities, from data vision and strategy all the way through data science.
Responsibilities include:
- Design greenfield data solution stacks in cloud and on-premises environments using modern data services, technologies, products, and industry best practices.
- Lead and architect the migration of legacy data environments to ensure performance and reliability.
- Assess data sources, data models, schemas, and data workflows to define scalable and maintainable enterprise data architectures.
- Assess existing ETL jobs, data pipelines, workflows, BI tools, and reporting solutions to develop scalable modernization strategies and enterprise data solutions.
- Address technical inquiries concerning customization, integration, enterprise architecture, and the general functionality of data products.
- Design enterprise database, data warehouse, and lakehouse solutions using Databricks across AWS, Azure, or GCP environments.
- Support an Agile software development lifecycle through architectural guidance and collaboration with cross-functional teams.
- You will contribute to the growth of our Data Exploitation Practice!
- Ability to hold a position of public trust with the U.S. Government.
- Experience supporting military data programs or holding a previous or active U.S. Government security clearance is preferred.
- 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.
