Software Engineer
Description
Roles and Responsibilities :
- Conceptual data models
- Logical data models
- Physical data models
- Facts and dimensions
- Star schemas
- Snowflake schemas
- Slowly Changing Dimensions
- Data warehousing
- Data lakes / lakehouses
- Metadata management
- Data lineage
- Data quality
- Reference and master data concepts
- Amazon S3
- Amazon Redshift
- AWS Glue / Glue Catalog
- Amazon Athena
- Apache Iceberg
Strong hands-on experience in enterprise data modelling.
Demonstrable experience developing:
Strong knowledge of dimensional modelling, including:
Strong understanding of relational data modelling and database design principles.
Strong SQL skills and the ability to analyse complex source data and existing database structures.
Experience developing source-to-target mappings and transformation rules.
Strong understanding of:
Experience modelling data for analytical, reporting and downstream consumption requirements.
Good understanding of modern cloud data technologies, preferably:
Experience working with structured and semi-structured datasets.
Strong analytical skills with the ability to understand complex business concepts and translate them into clear data structures.
Ability to communicate data models and definitions effectively to both business and technical stakeholders.
Job Requirements:
- erwin Data Modeler
- SAP PowerDesigner
- ER/Studio
Strong experience in Data Modelling, Data Warehousing and Enterprise Data Platforms.
Demonstrable experience developing enterprise-scale data models within complex organisations.
Experience working alongside Data Architects, Data Engineers, Business Analysts and business stakeholders.
Experience within Banking or Financial Services strongly preferred.
Knowledge of banking data domains such as Customer, Account, Product, Transaction, Finance, Risk and Treasury would be advantageous.
Good understanding of enterprise data governance and data-management principles.
Experience supporting the full development lifecycle from requirements and source analysis through modelling, engineering implementation, validation and production delivery.
Experience with recognised data-modelling tools such as:
Experience with enterprise data catalogue and metadata-management platforms.
Exposure to Data Vault 2.0 modelling.
Knowledge of data lakehouse and medallion architecture patterns.
Experience with AWS-based Enterprise Data Platforms.
Knowledge of Python or PySpark sufficient to understand physical transformations and validate engineering implementation.
Experience with regulatory and financial-services data models.
Experience defining enterprise business glossaries and common data definitions.