Technical Data Architect

Key Skills

Technical Recruitment, Amazon RedShift, Amazon Simple Storage Service (S3), Apache Cassandra, Apache Hadoop, AWS, Data Architecture, Data pipelines, Data science techniques, Design, DevOps, ETL frameworks, Git, IT, Lambda, NoSQL, Perl Programming, Planning and organization, PowerShell Programming, Project management, Python Programming, Ruby on Rails, Scrum Agile Methodology

Job Description

• Provide data architectural expertise across the data domain including interaction with application solutions on premise and in the cloud. This includes, but is not limited to, establishing technical data architectures and performing data integrations for relational, non-relational, multi-dimension solutions. Design, document, and communicate secure technical data solutions with the understanding of the holistic view for the enterprise.
• Create and maintain physical data models (Relational, Multi-Dimensional and Non-relational) using logical models and technical requirements to ensure these models meet the requirements and goals of the enterprise.
•  Collaborate with IT and the business to drive adoption of data tools. Give technical talks and demos on the newest abilities and enhancements of the enterprise level data solutions.

Requirements:

Advanced skills in SQL development and comfortable designing, writing, and maintaining complex SQL based ETL, which includes building large-scale batch and real-time data pipelines • Well-versed in designing data models and data management strategies to support analytical and modeling applications as well as Analytical and Data Science teams. • Strong background in data warehouse concepts and designs; ETL, Star Schema, Multi-Dimensional databases • Cloud computing, ideally Azure or (but not limited to); AWS, Redshift, Lambda, Cassandra • Familiar with at least one scripting language (PowerShell, JavaScript, Python, Ruby, Perl, Bash) • Strong data management and processing skills, including experience with Relational and Non-relational data stores (NoSQL, S3, Hadoop, etc.) • Ability to adjust designs to meet project objective and timelines while maintaining the vision and goals of solid database design principles. • Expert in eliciting solid data element descriptions and definitions and with physical data modeling tools and proficient in at least one (e.g., Erwin) • Experience with SOA, Service Bus technologies, and other data abstraction tools and access layers. • Strong communication skills both in written (technical documents, Jupyter notebooks) and spoken (meetings, presentations) forms • Efficiently work and produce results within the structure of project management methodologies (e.g., Agile, Scrum, Waterfall) • Demonstrate commitment to meeting organizational, department and team goals and objectives • Familiar with the DevOps, data pipeline and sharing code repositories with tools such as GIT