Data Engineer

Blueprint is a technology solutions firm headquartered in Bellevue, Washington, focused on leveraging technology to unlock value for organizations. As a Data Engineer on the Data Pipeline team, you will build and support large-scale analytics platforms and design data pipelines to facilitate data-driven decision-making.

Responsibilities:

• Design, build, and maintain scalable, high-quality data pipelines supporting both real-time and batch analytics workloads
• Develop and optimize ETL/ELT processes using modern cloud data technologies and orchestration tools
• Contribute to the design and evolution of lakehouse and warehouse data models that support analytics and reporting needs
• Partner with business, design, test, and development teams to understand data requirements and improve data capture strategies
• Help lead technical writing initiatives, including goal-setting, planning, and execution
• Write, edit, and maintain clear, concise, and well-structured technical documentation
• Create and improve documentation standards, organization, and knowledge-sharing processes
• Organize and curate documentation to ensure it is intuitive, discoverable, and up to date
• Advocate for consistent knowledge sharing across teams and studios
• Research and propose improvements to documentation workflows and solutions to existing pain points
• Apply modern engineering best practices to ensure reliability, scalability, and data quality across platforms

Requirements:

• 5+ years of professional experience working with SQL
• 5+ years of experience designing and implementing scalable ETL processes, including data movement and data quality tooling
• Hands-on experience with cloud-based data orchestration solutions (e.g., Azure Data Factory or equivalent)
• 3+ years of experience with modern big data analytics platforms, including: Data lakes, Distributed processing frameworks (e.g., Spark), Columnar storage formats such as Parquet
• 2+ years of experience building and supporting cloud-hosted data systems
• Strong understanding of data modeling, pipeline reliability, and performance optimization
• Experience building data pipelines using cloud-native analytics platforms and services (e.g., data factory tools, analytics warehouses, and Spark-based processing)
• Hands-on experience working with Delta Lake and transactional data lake formats
• Experience querying and integrating data from high-performance analytics engines (e.g., time-series or log-based systems such as Kusto/Azure Data Explorer)
• Exposure to AI/ML-focused data engineering use cases, including: Feature engineering and feature stores, Model training and serving datasets, Model monitoring and observability pipelines
• Experience preparing, governing, and securing datasets for modern AI applications, including: LLM and RAG workflows, Experimentation and A/B testing, Privacy-aware and compliant data access patterns