Senior Data Systems Analyst

Job Title : Senior Data Systems Analyst

Location : Santa Clara, CA (Onsite)

JC : 54079

Salary : $155k

Job Description :

Job Description - Senior Data Systems Analyst

The NVIDIA Operations organization is seeking an experienced data and Cloud technology professional for the position of a Data Systems Analyst(DSA) who performs complex, advanced work, designing and implementing data and advanced analytics solutions utilizing data warehousing skills that require a deeper understanding of Operations related business processes, data and analytics to support the development of Operations Data platform. This role requires close collaboration with various senior business and cross-functional stakeholders including IT, with a primary focus on enabling data solutions faster for the Operations organization.

Key Responsibilities

• Interact with stakeholders in Planning, Logistics, Services, Quality etc. to understand business processes, determine data requirements, and deliver e2e data solutions.
• Craft detailed user stories understanding the business requirements, metrics, dimensions, data fabrication logic, business rules, validation rules etc. with acceptance criteria and solutions required.
• Analyze the true source of data and map business requirements to data requirements by performing fit-gap analysis, talking to technical teams within Ops, IT and other cross-functional SMEs.
• Create a data dictionary documenting details about data elements, their definitions, formats, relationships, business logic etc. to be implemented ensuring consistency and clarity.
• Create ERDs, complex data model designs understanding the intricacies and relationships of data appropriate for staging stores / data lakes, data warehouses, and data marts. [DSA will create logical models defining high level relationships as they will have a very good understanding of the business process and data involved. The Data Architect's will create the physical models and perform refined data modelling].
• Translate and map the business requirements to data requirements. Work with the technical teams to implement solutions that will best meet the identified business needs.
• Work with the Data Engineers to implement the e2e data pipeline to address all the data processing requirements to create the bronze, silver and gold data products.
• Thoroughly test the delivered data products validating the data back to the respective source applications to ensuring data accuracy, technical and functional requirements.
• Define data governance, quality control, security policies around data duplication, data definitions, company-wide processes around security, privacy and access control. [DSA will collect and document these requirements in each of the user stories. The Data architect and Data Engineers will implement].
• Work independently, present designs and solutions to the team for review and implementation.

Must-Have Skills / Experience Required

Bachelor's or Master's degree in computer science or information systems, or equivalent experience with programming knowledge (SQL) and supporting Supply Chain Operations (Ops) data needs

Minimum of 10+ years of work experience working in a Data Warehousing setting implementing multiple projects using data lakehouse solutions and technologies.

7+ years of experience analyzing and dealing with Planning, forecasting, logistics, fulfillment related Ops data from SAP, Anaplan, Agile PLM, etc.

Experience of cloud environments (AWS, Azure, etc.) and enterprise database management tools (SAP BW / HANA) is required

Knowledge of ERP, CRM including SAP, Salesforce and expertise analyzing and navigating these applications is required

Experience in understanding and exploring Tableau, PowerBI, SAP BO reports is required

Positive interpersonal skills with ability to convey good verbal and written communication.

Ways to stand out from the crowd :

Proven experience leading multiple analytics projects in a dynamic, fast-paced environment

Data science / ML experience

Identify and implement improvements to current data management and reporting processes like data monitoring and data quality checks