Database architect • burlington on
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There is a 6 months contract opportunity for Data Warehouse Architect in Hamilton, Ontario.
Candidate must be enthusiastic about system architecture, big-data processing, data modelling,
advanced quantitative data-driven models, data visualization, and performance optimization in large scale databases.
Develop conceptual, logical, and physical data models as part of an Enterprise Data Warehouse.
Develop design standards, naming conventions, ETL processes, and test plans for the Enterprise Data Warehouse.
Standard working day is equal to 7.5 Hours.
Candidates will be expected to work onsite in Hamilton, Ontario, Canada at locations designated by HHSC from time to time;
Our project team will build and operate an Enterprise Data Warehouse that integrates data from dozens of different source systems and implement a leading analytics platform for data visualization.
These tools will help HHS harness our data to make smarter decisions about how we serve our patients and how we operate our hospital system.
Candidate must be enthusiastic about system architecture, big-data processing, data modelling, advanced quantitative data-driven models, data visualization, and performance optimization in large scale databases. Candidate can envision, and lead a team to implement architectures for high volume near-real-time ingestion, transformation and visualization of data while ensuring data quality, broad accessibility and high security, all while protecting the privacy of our most important stakeholders – our patients.
The prospect of working with large volumes of healthcare data is an ideal position for the candidate .
3.0 Develop conceptual, logical, and physical data models as part of an Enterprise Data Warehouse unifying administrative and clinical systems. The overall model should consider all source systems, but will focus on key areas initially such as Lab, Pharmacy, Patient Demographics, General Ledger;
4.0 Develop design standards, naming conventions, ETL processes, and test plans for the Enterprise Data Warehouse;
5.0 Design robust, reusable and scalable data solutions and pipeline frameworks to automate ingestion, processing and delivery of batch and real-time streaming data;
6.0 Ensure on-going alignment of new and existing data initiatives with the target state data architecture;
7.0 Define standards and best practices for enterprise data management framework;
8.0 Play an active role in all stages of a data solution life cycle: acquisition, ingestion, curation, publication, quality and metadata to ensure consistency with data management standards and best practices; and
9.0 Assist in the selection of data management tools required to expand our platform capability.
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