Lead AI-Driven Data Systems Analyst – Product Master Data

Franklin Templeton

San Mateo (CA)

Hybrid

USD 135,000 - 175,000

Full time

3 days ago

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Benefits offered by this job

Paid Time Off (PTO) – three weeks in第一

Health & dental benefits

401(k) with generous match

Employee stock investment plan

LEAP education assistance

Job summary

Franklin Templeton is seeking a Lead Systems Analyst in the Enterprise Data Analytics group to drive requirements, design, and end-to-end delivery for enterprise product master data initiatives. You will translate complex data needs into scalable, governed solutions across Finance, Investment, Sales, Product, and Distribution.

You will mentor junior analysts, partner with data engineers and architects, and apply an AI-first mindset to analysis and documentation.

Qualifications

Bachelor’s degree in Computer Science, Engineering, or related field.
10+ years in systems analysis, data analysis, or master data management roles.
Ability to design scalable data solutions and lead complex data initiatives.
Domain expertise in enterprise product data and data governance practices.
Proven leadership, mentoring, and cross-functional influence.
Experience driving data governance, ownership models, and enterprise data standards.
Collaborative experience with business, architecture, and engineering teams.

Responsibilities

Lead requirements gathering, analysis, and solution design for enterprise product master data initiatives across systems.
Drive end-to-end delivery of data initiatives, including planning, coordination, and execution across cross-functional teams.
Define and manage data mappings, data flows, transformation rules, and integration patterns across source and target systems.
Identify and document business processes, workflows, and data dependencies for scalable data solutions.
Drive data governance practices by defining standards and ensuring data quality.
Partner with stakeholders to identify data owners and data stewards and establish accountability for data domains.
Collaborate with data engineers, architects, and application teams to deliver scalable data solutions.
Perform root cause analysis on complex data issues and drive resolution.