Data modeller
1 week ago
Role: Data Modeler
About Tredence:
Tredence focuses on last-mile delivery of powerful insights into profitable actions by uniting its strengths in business analytics, data science and software engineering.
The largest companies across industries are engaging with us and deploying their prediction and optimization solutions at scale. Head quartered in the San Francisco Bay Area, we serve clients in the US, Canada, Europe, and South East Asia.
We are seeking an experienced data scientist who apart from the required mathematical and statistical expertise also possesses the natural curiosity and creative mind to ask questions, connect the dots, and uncover opportunities that lie hidden with the ultimate goal of realizing the data's full potential.
Roles and responsibilities
• Solution Architect for Data modelling – Understanding of Enterprise datasets – Sales, Procurement, Finance, Supply Chain, Logistics, R&D, Advanced Planning systems (SAP/Oracle etc); Have worked for minimum 5 years for Data projects ( Upgradation, Migration, Building Data lake foundation, Maintenance etc)
• Collaborate with the product/business teams, understand related business processes and document business requirements and then write high level specifications/requirements for DEs
• Develop logical/physical data models as per Medallion architecture/EDW/Kimball model; Scout for right grain of data either in True source systems or in Data WHs and build reusable data models in intermediary layers before creating physical consumable views from data mart
• Understand Cloud architecture, solution components in Cloud, DevOps, DataOps; Data Governance, Data Quality frameworks, Data Observality and the candidate should be: o
Familiar with DevOps process o Knowing how to check existing tables, data dictionary, table structures o Experienced with normalizing tables o Having good understanding of Landing, Bronze, Silver and Gold layers and concepts o Familiar with Agile techniques
• Create business process map, user journey map and data flow integration diagrams; Understand Integration needs (Integration through API, FTP, webservices and SFTP) for E2E deployment of models
• Stakeholder management with data engineering, product owners, central data modelling team, data governance & stewards, Scrum master, project team and sponsor.
• Ability to handle large implementation program with multiple projects spanning over an year.
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