POSITION SUMMARY:
Integrated within the People, Culture, and Strategy Pillar, the Enterprise Data and Analytics Architect will provide strategic and technical leadership in data-strategy, data-processes, and drive the development of the organization’s data architecture, its platform, models, and data-governance. The architect will leverage his/her/their understanding of leading industry standards in delivering modern data solutions, data ingestions/data pipelines, data-quality, data-governance and implementation of Enterprise Data Strategy supporting SJHH’s Business Intelligence (BI) platform. The candidate will provide expertise and risk mitigation, collaborate with various key internal and external business stakeholders to ensure SJHH’s Enterprise Data Architecture is optimized for all corporate and BI requirements including data storage, reporting and analytics.
QUALIFICATIONS:
- Bachelor’s Degree in quantitative areas such as Computer Science, Information Management, Big Data & Analytics, or a related field required. An equivalent combination of education and experience will be considered
- Master’s Degree in quantitative areas such as Computer Science, Information Management, Big Data & Analytics, or a related field preferre
- Certification in Microsoft suite of products (SQL Server, SSIS, SSRS/Power BI Report Server, SSAS) required
- Certification in Microsoft Azure suite of products (Azure SQL Databases, Azure Data Factory, Azure Databricks/Apache Spark, Azure Data Lake, Synapse Analytics, Microsoft Power BI/Fabric) required
- Minimum ten (10) years experience as a Data Architect or similar required
- Experience as an Enterprise Data Architect working on organization-wide systems integration and transformation initiatives required
- Advanced understanding of and 10+ years practice implementing Enterprise Data Architecture platforms, concepts, patterns, and artifacts, using on-prem, cloud or hybrid platforms
- Demonstrated ability to lead large scale data architecture, data engineering and analytics projects with practical experience throughout the entire lifecycle
- Demonstrated ability to implement business vision, strategy, roadmaps, governance and quality methods and practices
- Advanced technical knowledge in the areas of implementing and productionizing reporting and analytics enterprise platforms storage and compute stacks, using on-premises, cloud, and hybrid platforms
- Deep technical knowledge and experience with data engineering projects, including data pipelines planning, orchestration, implementation, and maintenance in large, mission critical environments and using a variety of data sources, transformations, and destinations
- Advanced data modeling experience in a variety of environments along the analytics continuum (OLTP, multidimensional and tabular semantic modeling)
- Strong verbal, written and presentation skills and ability to collaborate across diverse teams of stakeholders
- Ability to conceptualize complex ideas into key artifacts required to develop data architectures that are consistent with the organizational architecture standards and technology strategy
- Ability to estimate development efforts from resourcing and budgeting perspectives
- Advanced knowledge and experience planning, implementing, and maintaining large scale data solutions on-premises using the Microsoft suite of products (SQL Server, SSIS, SSRS/Power BI Report Server, SSAS)
- Advanced knowledge planning, implementing, and maintaining large scale data solutions in the cloud, using the Microsoft Azure suite of products (Azure SQL Databases, Azure Data Factory, Azure Databricks/Apache Spark, Azure Data Lake, Synapse Analytics, Microsoft Power BI/Fabric)
RESPONSIBILITIES:
- Support the organizational decisions on analytics roadmap strategy and drives the implementation of the end-to-end modern data and analytics platform
- Own, develop and maintain the data architecture framework and artifacts, such as enterprise-wide conceptual, logical, and physical models for key initiatives and projects driven by the corporate data and analytics strategy
- Authority on Data Engineering and Analytics processes framework, development, and lifecycle practices
- Own and maintain the Data Engineering and Analytics toolset and development framework (tools, collaboration, source control and documentation standards)
- Develop standard connectors, pipelines, data models and other analytics assets templates to ensure consistency across the data engineering and analytics operations
- Develop and maintain the support, monitoring and troubleshooting framework for the data engineering and analytics operations
- Keep informed, prototype and safely introduce the latest advancements and innovations in the analytics industry, in line with organizational strategic path, while ensuring cost effectiveness
- In collaboration with Enterprise and Infrastructure architecture groups, ensure interoperability capabilities, performance and scalability, reliability and availability, data consistency and technology risk are addressed in the data architecture
- Authority on Analytics Data Modeling and Analytics Delivery Model framework, including design patterns and schemas for:
Centrally delivered analytics content; Analytics endpoints for end-user and Process consumption; and Self-service modeled content, including user community training and support - Authority on Data and Analytics infrastructure – organizational fit, sizing, planning, deployment strategies, support, and maintenance framework for toolset, storage, compute, and operational data endpoints
- Advise and collaborate on operational workflows across the organizational functions
- Authority on Analytics data governance and Analytics and data protection for data at rest and in transit, analytics security, quality, and accessibility, based on best industry practice and organizational standards
- Advise and collaborate on organizational data governance and data protection