Data Warehouse Architect
Belgrade Full time
One of the largest investors in Serbia, a systemic and responsible bank, operational more than 15 years on the market, due to the intensive activities is hiring a Data Warehouse Architect.
- Analyze all data warehouse architecture goals and identify requirements for the same.
- Understand business requirements and translate into appropriate KPIs
- Collaborate and work with end-users to ensure that data and reports meet their business needs.
- Prepare design for all metadata according to various ETL processes.
- Develop both physical and logical data models.
- Prepare designs for database systems and recommend improvements for performance.
- Develop various ETL processes and prepare OLAP cubes.
- Maintain and develop various database scripts and tools to facilitate automation process.
- Perform troubleshoot on all ETL processes and resolve issues effectively.
- Design and monitor end-user reporting tools and systems, ensuring reports are accurate and up-to-date.
- Participate in setting up of data governance process, data cleaning and validation
- Introduce new technologies in DWH and reporting segment
- At least 7 years of Data Warehouse ETL design, development and maintenance with a successful implementation of large Enterprise BI solutions
- Min. 5 years of experience with RDBMS platforms (ie. Oracle, Teradata, DB2, MS SQL Server), SQL, ETL tools (Talend, Informatica PC, Business Objects Data Integrator, SSIS and others), big data architecture and related technologies (ie Spark)
- Developing solution architecture using business stakeholders’ requirement and transforming into conceptual data models, including socializing and implementing logical and physical
- DW/data mart data models; identifying data sources and metadata (including business rules, definitions, attributes, etc.) to deliver analytic requirements
At least two full lifecycle implementations, BI/SQL, DW/SQL/ETL/Modeling tools and other related technologies involving:
- Design and development of data models, extraction code and cleansing routines utilizing typical data quality functions, i.e. standardization, transformation, rationalization, linking and matching that leverages data standards, processes and technology management for data, master data, and metadata; and, data security (including data masking and related data protection techniques)
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