Job
Description
Role & responsibilities Job Summary We are looking for experienced Data Modelers to support large-scale data engineering and analytics initiatives. The role involves developing logical and physical data models, working closely with business and engineering teams to define data requirements, and ensuring alignment with enterprise standards. • Independently complete conceptual, logical and physical data models for any supported platform, including SQL Data Warehouse, Spark, Data Bricks Delta Lakehouse or other Cloud data warehousing technologies. • Governs data design/modelling documentation of metadata (business definitions of entities and attributes) and constructions database objects, for baseline and investment funded projects, as assigned. • Develop a deep understanding of the business domains like Customer, Sales, Finance, Supplier, and enterprise technology inventory to craft a solution roadmap that achieves business objectives, maximizes reuse. Drive collaborative reviews of data model design, code, data, security features to drive data product development. • Show expertise for data at all levels: low-latency, relational, and unstructured data stores; analytical and data lakes; SAP Data Model. • Develop reusable data models based on cloud-centric, code-first approaches to data management and data mapping. • Partner with the data stewards team for data discovery and action by business customers and stakeholders. • Provides and/or supports data analysis, requirements gathering, solution development, and design reviews for enhancements to, or new, applications/reporting. • Assist with data planning, sourcing, collection, profiling, and transformation. • Support data lineage and mapping of source system data to canonical data stores. • Create Source to Target Mappings (STTM) for ETL and BI developers. Preferred candidate profile Expertise in data modelling tools (ER/Studio, Erwin, IDM/ARDM models, CPG / Manufacturing/Sales/Finance/Supplier/Customer domains ) • Experience with at least one MPP database technology such as Databricks Lakehouse, Redshift, Synapse, Teradata, or Snowflake. • Experience with version control systems like GitHub and deployment & CI tools. • Experience of metadata management, data lineage, and data glossaries is a plus. • Working knowledge of agile development, including DevOps and DataOps concepts. • Working knowledge of SAP data models, particularly in the context of HANA and S/4HANA, Retails Data like IRI, Nielsen Retail.
