Data Warehouse Architect - OLAP/ETL (5-10 yrs)

Role - Data Warehouse Architect.

Years of Experience - 5 to 10 years.

Working Mode Work from Office Only.

Office Shift Timings - General shift (10 am-7 pm).

Roles and Responsibilities :

- Understand the company's present data infrastructure, identify gaps, and develop plans to integrate the current systems with the desired future model.

- Designing the data warehouse initiatives and data warehousing systems also recommends measures to improve performance for all metadata relating to various Extract/Transform/Load (ETL) processes.

- Develop as many ETL processes as needed and prepare Online Analytical Processing (OLAP) cubes also maintain database scripts and tools to make automation run smoother.

- Analyze every data warehouse architecture goal and identify what skill requirements they require.

- Monitor processes and develop plans to expedite gathering and accessing all relevant metadata.

- Manage the appropriate status reports and submit them to management.

- General responsibilities for implementing or maintaining the Noesys Information Security policy, as well as a specific responsibility for the protection of assets, or the execution of security processes or activities.

Required Skills :

- Must have a deeper understanding of Data Modeling, SQL, OLAP, and ETL, including practical experience with at least one ETL tool, such as SSIS.

- Must understand a BI and reporting platform, an awareness of industry trends and direction in BI/reporting, and how it can apply to an organization's product strategies.

- Should be proficient in programming languages and have experience working with database platforms, preferably Oracle.

- Should be familiar with best practices and automation of processes like building, configuration, deployment, documentation, testing, and monitoring.

- Added Advantage for data warehouse architect Certification At least One DWH project must be executed end to end.

Mandatory Skills :

- Designing the data warehouse initiatives and data warehousing systems also recommends improving performance for all metadata relating to various Extract/Transform/Load (ETL) processes.

- Develop as many ETL processes as needed and prepare Online Analytical Processing (OLAP) cubes also maintain database scripts and tools to make automation run smoother.

- Analyze every data warehouse architecture goal and identify what skill requirements they require.