Sr. Process Transformation Business Intelligence Data Analyst

Job Description

Do you want to use cutting edge technology to modernize and transform critical business processes? Are you able to collaborate with many different stakeholders to identify insights from large datasets and tell the story to leadership? The Process Transformation Business Intelligence Data Analyst works with stakeholders to solve some of the most difficult problems facing Global Purchasing and Supply Chain (GPSC) by automating manual processes, streamlining data collection, and identifying insights from disparate data sources.

A successful Process Transformation BI Data Analyst is customer focused and able to identify improvement opportunities and work with the right organizations to implement them. Customers will come from a wide variety of service lines that each have their own unique processes and systems. The Process Transformation BI Data Analyst will need to be a quick learner and willing to challenge current processes and promote continuous improvement.

This role is responsible for automating business processes and delivering self-service reports and dashboards for the GPSC organization. The ideal candidate can quickly learn and effectively use ETL and dashboarding tools such as KNIME, Power Automate, and Power BI, as well as perform data engineering using languages like SQL or Python to create simple datasets that can be used for a variety of reporting. Familiarity with agentic AI and large language model (LLM) applications is also preferred.

Responsibilities:

- Collaborate with the business, IT, and analytics community to craft, prioritize and document opportunities to provide better insights, identify data gaps, and improve efficiencies

- Lead and support service line modernization projects from requirement gathering through user-acceptance

- Analyze and create substantial insight for business use and decisions, understanding large amounts of data from structured and unstructured data sources

- Train and support the business in the use of self-service tools and dashboards and help the business understand the available data fields and logic

- Prepare documentation and analytical reports that effectively summarize results, assumptions, and conclusion

Additional Job Description:

Education/Experience

- Minimum 5 years of relevant industry experience

- Bachelor’s degree required in data analytics, engineering, computer science, operations management, supply chain or related field

Qualifications:

- Self-starter with the ability to influence others, working in teams and independently with customers, cohorts, and partners across many different physical locations

- Demonstrated ability to use a dashboarding tool such as Power BI to build customer \facing deliverables

             

- Experience extracting large datasets using SQL

- Working knowledge of Extract Transform Load (ETL) best practices and experience transforming data with Python, Power Query, KNIME or a similar tool

Preferred Qualifications:

             

- Knowledge of Source to Pay processes and working knowledge of SAP Ariba, SAP MM, or SAP FI

- Experience using statistical programming languages to create advanced analytics like python, R, or SAS

- Working knowledge of agentic AI and LLM AI and a curiosity or demonstrated success with business process automation.



GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.

This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.



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