Sr. Analyst, Master Data Management

LinkedIn•Sunnyvale, CA

22h•$98,000 - $158,000•Hybrid

About The Position

LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. This role will be based in San Francisco. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. We’re hiring a Sr. Analyst, Master Data Management (MDM) to help build and run the master data capabilities that keep Finance operating on consistent, reliable foundations. This role is about getting the basics right at scale: defining standards, improving data quality, enabling clean downstream reporting/automation, and driving the day-to-day operational rigor required to keep core entities accurate and usable across systems. You’ll work across Finance, Finance Technology, Engineering, and upstream source owners to manage and improve core financial and corporate master data domains (e.g., chart of accounts, cost centers, suppliers, customers, products), ensuring they are governed, auditable, and designed to support both operational workflows and analytical use cases.

Requirements

Education: Bachelor’s degree in Finance, Accounting, Information Systems, Data/Analytics, Engineering, or a related field (or equivalent practical experience).
Experience: 5+ years in master data, data governance, data operations, finance systems, or related roles working with business-critical datasets.
Data skills: Experienced analytical skills and comfort working with large datasets; proficiency in Excel/Sheets and SQL (2+ years) for validation, reconciliation, and investigation.
Process rigor: Experience operating structured processes (intake, approvals, audits, SOPs) with high attention to detail.
Stakeholder management: Ability to partner with both technical and non-technical teams, translate business needs into data standards, and drive alignment.

Nice To Haves

Finance master data experience: Hands-on experience with finance master data domains (cost centers / department hierarchies, Chart of Accounts,, legal entities, currency) and understanding of how they impact transaction flow, GAAP, and management reporting.
ERP familiarity: Experience working with ERP/finance platforms (Oracle EPM / EDM, SAP, or similar) and understanding of finance data models and hierarchies.
Governance frameworks: Familiarity with stewardship models, controls, and governance practices (RACI, data ownership, data dictionaries, quality rules, change control).
Data quality tooling/monitoring: Experience building or using data quality checks, exception reporting, lineage documentation, or observability mechanisms.
Automation mindset: Experience improving workflows through automation (Power Platform or similar) and standardizing repeatable processes.
Change management: Comfort driving adoption of standards across distributed teams and influencing without authority.

Responsibilities

Own master data operations for key domains: Support the intake, creation, change management, and lifecycle of core finance master data elements (including validation, approvals, and documentation
Implement and manage critical master data in Oracle EDM to support financial budgeting, strategic planning, and management reporting, including structures such as Cost Center, Department, and Product Hierarchies.
Drive data quality and standardization: Define and enforce business rules; identify quality issues (duplicates, hierarchy drift, missing attributes, inconsistent naming), prioritize fixes, and work with system owners to remediate root causes.
Build the governance muscle: Help operationalize policies, standards, and processes (RACI, controls, approvals, stewardship routines, periodic reviews) that keep master data healthy over time.
Partner across systems and teams: Collaborate with Finance Technology, Engineering, and upstream teams to align on data models, integrations, and processes—ensuring master data flows cleanly across tools and platforms.
Enable reporting, automation, and analytics: Ensure master data is structured and documented in a way that supports semantic layers, metric definitions, automation workflows, and reconciliation needs.
Improve tooling and workflows: Create lightweight solutions (templates, validations, dashboards, exception queues, status tracking) to increase throughput and reduce errors in master data processes.
Establish transparency and trust: Maintain clear documentation (definitions, hierarchies, attributes, ownership), publish guidance for requesters, and communicate changes and impacts to stakeholders.