We are looking brand new Data Analytics team for new customer.
In essence, client is leveraging the Azure PaaS platform with all kind of (business) data to build an advanced analytics platform aiming at delivering better insights and applications to the business.
The platforms are continuously being enhanced to support (additional) CI/CD and validated learning environment for science, machine learning and AI capabilities for all areas customer-facing like digital omni-channel interaction and commerce, commerce relevance, personalisation, loyalty and marketing and non-customer-facing like assortment optimization, supply chain optimization, external parties and IoT.
We will be working on end to end functionality including architecture, data preparation, processing and consumption by systems.
The data modeler designs, implements, and documents data architecture and data modeling solutions, which include the use of relational, dimensional, and NoSQL databases. These solutions support enterprise information management, business intelligence, machine learning, data science, and other business interests.
The successful candidate will:
- 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.
- Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).
- Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models
- Define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
- Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.
- Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.
- Work closely with Data Architect, Functional Analyst and Business Analyst to understand and translate business needs into data models supporting long-term solutions.
- Work with the Data Engineering team to implement data strategies, build data flows and develop conceptual data models.
- Create logical and physical data models using best practices to ensure high data quality and reduced redundancy.
- Maintain conceptual, logical and physical data models along with corresponding metadata.
- Develop best practices for standard naming conventions and coding practices to ensure consistency of data models.
- Evaluate data models and physical databases for variances and discrepancies.
- Validate business data objects for accuracy and completeness.
- Analyze data-related system integration challenges and propose appropriate solutions.
- Develop data models according to company standards.
- Review modifications to existing software to improve efficiency and performance.
- Examine new application design and recommend corrections if required.
- Bachelors or masters degree in computer/data science technical or related experience.
- 5+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols) on physical and conceptual data models.
- Experience with data modeling tools (at least one is a must):
o Erwin Data Modeler
o SAP Power Designer
o Sparx Enterprise Architect
- Designed/modeled enterprise data platforms (HR, admin, financial, procurement, logistics, supply chain, physical inventory, enterprise asset management etc.)
- Proven track with the implementation of common data model (preferably retail models)
- Experience working with UML
- Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.
- Good knowledge of metadata management, data modeling, and related tools (Erwin or ER Studio or others) required.
- Advanced troubleshooting skills.
- Experience in team management, communication, and presentation.