D.A. Davidson Companies is looking to expand and optimize its data and data pipeline architecture, as well as monitor, troubleshoot, and performance tune data flows for data collection, integration, and provisioning.
Requirements
- 2 or more years of Data Engineering experience in an oversight capacity
- Experience with Microsoft cloud-based technologies in a hybrid architecture
- Advanced working TSQL knowledge and experience working with relational databases, query authoring, stored procedure development, end-to-end data pipeline development
- Experience performing operational analysis on internal and external data and processes to uncover failure points and identify opportunities for improvement
- Strong analytic skills related to working with complex, multi-layered data pipelines and dependent datasets
- Holistic awareness of processes supporting data preparation, metadata, dependencies, lineages, compliance and workload management
- Extensive experience with SQL-based syntax
Responsibilities
- Design, implement and maintain a robust, efficient and compliant data pipeline architecture
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build and maintain a service-based architecture for the publication of consumable data and information objects
- Work with data architects and data analysts to enhance and evolve the data environment
- Work with Business and IT stakeholders to assist with data-related technical issues and support their data and information requirements
- Define and develop frameworks, guidelines, best practices and examples to ensure data structures and integration processes are accurate, compliant and secure
- Facilitate the development and implementation of data quality standards, data protection standards and adoption requirements across the enterprise
Other
- Extensive domain-specific experience in the Financial Services industry
- Experience facilitating calls to capture end-user requirements with technical and non-technical users
- Understanding of data modeling concepts like: Conceptual data modeling, Logical data modeling, Relational data modeling, Dimensional and multi-dimensional data modeling
- Understanding of meta-model and metadata management requirements
- Experience with Agile, DevOps and DataOps methods and technologies: Source Code Control, Versioning, Test Automation, Continuous Integration, Automated Deployment, Recoverability, Telemetry, etc.
