Understand and translate business needs into data models supporting long-term solutions.
Work with the Application Development team to implement data strategies, build data flows and develop conceptual data models.
Create and maintain conceptual, logical and physical data models using best practices to ensure high data quality and reduced redundancy, along with corresponding metadata.
Optimize and update logical and physical data models to support new and existing projects.
Develop best practices for standard naming conventions and coding practices to ensure consistency of data models.
Recommend opportunities for reuse of data models in new environments.
Perform reverse engineering of physical data models from databases and SQL scripts.
Evaluate data models and physical databases for variances and discrepancies.
Validate business data objects for accuracy and completeness.
Analyse data-related system integration challenges and propose appropriate solutions.
Develop data models according to company standards.
Guide System Analysts, Engineers, Programmers and others on project limitations and capabilities, performance requirements and interfaces.
Review modifications to existing software to improve efficiency and performance.
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Examine new application design and recommend corrections if required.
Assist with and support setting the data architecture direction (including data movement approach, architecture / technology strategy, and any other data-related considerations to ensure business value).
Ensuring data architecture deliverables are developed, ensuring compliance to standards and guidelines, implementing the data architecture, and supporting technical developers at a project or business unit level.
Coordinate and consult with the project manager, client business staff, client technical staff and project developers in data architecture best practices and anything else that is data related at the project or business unit levels.
The successful candidate will:
Provides technical expertise in needs identification, data modelling, data movement and transformation mapping (source to target), automation and testing strategies, translating business needs into technical solutions with adherence to established data guidelines and approaches from a business unit or project perspective.
Be responsible for the development of the conceptual, logical, and physical data models, the implementation of RDBMS, operational data store (ODS), data marts, and data lakes on target platforms (SQL/NoSQL).
Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices.
The candidate must be able to work independently and collaboratively.
Leadership not only in the conventional sense, but also within a team we expect people to be leaders. Candidate should elicit leadership qualities such as Innovation, Critical thinking, optimism/positivity, Communication, Time Management, Collaboration, Problem-solving, Acting Independently, Knowledge sharing and Approachable.
7-10 Years industry implementation experience with one or more data modelling tools such as Erwin, ERStudio, PowerDesigner etc.
Minimum of 8 years of data architecture, data modelling (Data Vault and Dimensional, 3NF) or similar experience.
5-7 years of management experience required.
5-7 years consulting experience preferred.
Experience working with dimensionally modelled data.
Bachelor’s degree or equivalent experience, Master’s Degree Preferred.
Understanding of cloud (Azure, AWS, GCP, Snowflake preferred) and on premises architectures.
Experience in data analysis and profiling.
Strong data warehousing and OLTP systems from an integration perspective.
Strong understanding of data integration best practices and concepts.
Strong SQL skills required scripting preferred.
Strong Knowledge of all phases of the system development life cycle.
Experience with major database and big data platforms (e.g. RDS, Aurora, Redshift, Databricks, MySQL, Oracle, PostgresSQL, Hadoop, Snowflake, etc.).
Understanding and experience with major Data Architecture philosophies (Dimensional, ODS, Data Vault, etc.).
Understanding of modern data warehouse capabilities and technologies such as real-time, cloud, Big Data
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