Pim Data Analyst Jobs in Rochester, NY (NOW HIRING)

Company Description
The transition from Bosch to Radionix is now complete - providing a future-focused trajectory for our Radionix and Bosch product portfolios and exclusive benefits for our valued partners.
The Radionix name holds a special place in the hearts of our associates, dealers, distributors, and customers alike. The new Radionix vision - Mission control for your security - encapsulates our focus on empowering users with an intrusion system that seamlessly integrates every layer of security, from our intrusion system products to our G Series panels to our reimagined Remote Security Manger app.
Fairport, NY is the headquarters for the Radionix sales & marketing organization in the United States and serves as the primary site for research and development, engineering, quality, and product management for intrusion detection systems, including state-of-the-art sensors, control panels, software, communications and more.
Job Description
The Senior Data Manager is responsible for leading the transformation of the business-unit data landscape from a fragmented, complex and manually maintained environment into a modern, governed, lean and business-driven data capability. The role owns the roadmap for data management and master data management across core business domains and ensures that data ownership, quality, lifecycle processes and reporting foundations are clearly defined and embedded into operating processes.
This role is broader than traditional MDM tooling or master-data administration. It covers customer, product, material, vendor, pricing, installed-base and reference data domains, while also enabling analytics, Power BI/reporting, data platform roadmap, AI readiness, AI initiatives and data integration governance across CRM, HubSpot, ERP, PIM, customer portal, engineering applications, operational tools and other critical systems.
Scope of Ownership:

• Data Management Strategy: Data roadmap, operating model, ownership model, governance cadence
  • Move from fragmented data handling to governed, business-owned data capability
• Master Data Domains: Customer, Product, Material, Vendor, Pricing, Installed Base, reference data
  • Define ownership, lifecycle, quality rules and authoritative sources per domain
• CRM & Commercial Data: HubSpot, SAP CRM, customer portal, sales, marketing, service and installer journeys
  • Ensure clean customer, account, contact, opportunity and service-related data for commercial execution
• Product, PIM & Engineering Data: PIM, product data, engineering attributes, product lifecycle and operational data
  • Connect product and engineering data needs with commercial, service, ERP and analytics use cases
• ERP & Operational Data: ERP, supplier, material, pricing, order, installed-base and supply-chain data
  • Improve consistency and governance across core operational systems
• Data Quality & Lifecycle: Creation, validation, enrichment, maintenance, archiving and remediation workflows
  • Reduce defects, manual corrections and shadow Excel-based logic
• Analytics & Reporting Enablement: Power BI, dashboards, data products, data quality metrics and reporting datasets
  • Enable trusted reporting, clean KPIs and decision-ready data
• Data Platform & Integration: APIs, integration flows, data platform roadmap, Snowflake/reporting capability where relevant
  • Ensure data flows are governed, documented and reusable
• Compliance & Data Controls: GDPR, export controls, sanctions screening on vendor master, auditability
  • Embed regulatory and control requirements into data processes and tooling

Key Responsibilities:
Data Management Strategy & Transformation

• Define and execute the data management and MDM transformation roadmap for the business unit.
• Transition the data landscape from complex, fragmented and carve-out-driven structures toward a lean, governed and scalable operating model.
• Establish a pragmatic data management framework aligned with business priorities, enterprise architecture and central data governance principles.
• Define the target-state data operating model, including roles, ownership, decision rights, stewardship responsibilities and escalation paths.
• Translate strategic data priorities into executable initiatives, milestones, KPIs and measurable business outcomes.

Master Data Domain Ownership

• Define and govern master data domains including Customer, Product, Material, Vendor, Pricing and Installed Base.
• Establish clear data ownership and stewardship models per domain, including business owners, data stewards and system owners.
• Define authoritative sources, golden-record principles and single-source-of-truth expectations across CRM, HubSpot, ERP, PIM, customer portal and operational systems.
• Create and maintain data definitions, business rules, data standards and lifecycle requirements for critical data objects.
• Support clean handover of master data between commercial, operational, service, finance, engineering and reporting processes.

Data Governance, Quality & Lifecycle Management

• Implement end-to-end data governance covering roles, responsibilities, workflows, validation rules, data standards and issue management.
• Define and track data quality KPIs for completeness, accuracy, consistency, timeliness, duplication and process compliance.
• Establish workflows for data creation, validation, enrichment, approval, maintenance, remediation and decommissioning.
• Reduce shadow data processes, Excel-based logic, duplicate master data and manual correction loops.
• Ensure data governance is embedded into daily operations rather than treated as a one-off clean-up project.

CRM, Customer Portal & Commercial Data Enablement

• Ensure governed data structures and master data feeds enable CRM (HubSpot), customer portal, BI and AI use cases.
• Partner with Sales, Marketing, Customer Service, Commercial IT Product Ownership and Finance stakeholders to improve commercial data quality.
• Support CRM migration and commercial platform transformation by defining data requirements, migration quality criteria and reconciliation expectations.
• Improve account, contact, customer hierarchy, opportunity, service and installed-base data needed for commercial execution and customer lifecycle management.
• Support clean reporting and customer-data readiness for business reviews, due diligence and buyer-readiness activities.

Product, PIM, Engineering & Operational Data

• Extend data management beyond CRM and ERP into PIM, engineering, product and operational applications where relevant.
• Define product and material data standards that support commercial, supply-chain, service, engineering and reporting use cases.
• Work with Product, Engineering, Operations and Supply Chain stakeholders to improve data structures, ownership and lifecycle controls.
• Support rationalisation of fragmented product and operational data processes across legacy systems and business-owned tools.
• Ensure engineering and operational data can be integrated into future digital, analytics and automation capabilities.

Data Architecture, Integration & Tooling

• Collaborate with Enterprise Architecture and Technical Applications & Integration leadership to define the role of MDM and data management in the target architecture.
• Define data flows, interface ownership, data lineage expectations and integration requirements across CRM, ERP, PIM, portal, data platform and reporting layers.
• Evaluate and support selection or implementation of MDM/data management tools where appropriate, subject to architecture and business fit.
• Promote API-based, reusable and well-documented data integration patterns.
• Ensure data platform and reporting roadmap decisions are aligned with master data quality and ownership maturity.

Analytics, Reporting & AI Enablement

• Enable trusted reporting by improving data definitions, master data quality, source alignment and KPI consistency.
• Partner with reporting and analytics stakeholders to support reporting dashboard enablement and data-product development.
• Define data quality dashboards and management reporting for data ownership, remediation status and issue trends.
• Ensure AI and automation use cases are built on reliable, governed and fit-for-purpose data.
• Support the transition from ad hoc reporting to reusable, governed and business-owned data products.

Compliance, Controls & Data Risk

• Embed regulatory, privacy and control requirements into master data processes and tooling.
• Support GDPR-related data governance requirements and data handling expectations in cooperation with legal, privacy and security stakeholders.
• Coordinate data requirements related to export controls and sanctions screening on vendor master where relevant.
• Ensure auditability of critical data changes, ownership decisions and remediation actions.
• Identify and manage data risks affecting business continuity, revenue reporting, compliance, customer experience and buyer readiness.

Stakeholder, Vendor & Change Management

• Act as trusted data partner for Sales, Marketing, Customer Service, Operations, Engineering, Product, Finance and IT stakeholders.
• Facilitate workshops to define data ownership, data issues, root causes, target processes and remediation priorities.
• Steer external MDM, data governance, reporting and transformation partners where required.
• Support organisational change by making data ownership practical, visible and embedded into business processes.
• Communicate data priorities, trade-offs, quality issues and remediation progress clearly to business and IT leadership.

Strategic Value Realisation

• Convert data-management and MDM elements of the IT strategy into a practical execution roadmap.
• Use data quality and governance to support revenue enablement, cost reduction, risk mitigation and buyer readiness.
• Contribute to application rationalisation, integration control and platform simplification by clarifying data dependencies and ownership.
• Track measurable improvement in data quality, reporting reliability, master-data process performance and business adoption.
• Help move the organisation from ad hoc data clean-up to permanent data capability ownership.

Qualifications
Required Experience

• 7-10+ years of experience in data management, master data management, data governance, enterprise data management, analytics enablement or related roles.
• Proven experience in large-scale data transformation, MDM implementation, data quality improvement or data operating model programmes.
• Strong understanding of MDM concepts, data models, data lifecycle processes and governance frameworks.
• Experience defining data domains, business rules, ownership models and stewardship responsibilities.
• Experience working across CRM (HubSpot), ERP, PIM, reporting and integrated application landscapes.
• Experience with API-based, integrated architectures and cross-system data flows.
• Strong stakeholder management across business and IT, with ability to simplify complex legacy structures into lean target models.

Preferred Experience

• Experience with MDM or data management tools such as Stibo STEP, Informatica or comparable platforms.
• Experience with Power BI (or similar tools), governed reporting, data quality dashboards or data-product enablement.
• Experience with CRM migration, commercial data quality, customer master data or customer portal data enablement.
• Experience with product, PIM, material, vendor, pricing or installed-base data domains.
• Experience in manufacturing, security technology, industrial technology, engineering or connected product environments.
• Experience supporting carve-out, post-M&A, buyer-readiness, due diligence or transitional service model contexts.
• Experience working with central data governance teams in a federated business-unit operating model.

Technical & Domain Knowledge

• MDM & Data Management: Master data domains, golden record, data standards, business rules, stewardship and lifecycle management
• Data Governance: Ownership model, data policies, governance forums, issue management, decision rights and data controls
• Data Quality: Completeness, accuracy, consistency, duplication, timeliness, remediation workflows and quality dashboards
• CRM & Commercial Data: HubSpot/SAP CRM concepts, customer/account/contact data, installed base and commercial reporting needs
• ERP & Operational Data: Material, vendor, pricing, order, supply-chain and finance-related master data dependencies
• PIM & Product Data: Product information structures, product attributes, catalogue data, product lifecycle and PIM integration concepts
• Analytics & Reporting: Power BI/dashboard enablement, data definitions, KPI consistency and trusted reporting foundations
• Architecture & Integration: APIs, data interfaces, lineage, integration patterns, source-of-truth principles and data platform roadmap
• Compliance & Controls: GDPR, export controls, sanctions screening, auditability and data change control expectations

Additional Information
The U.S. base salary range for this full-time position is $110,000-$139,000. Within the range, individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc.
In addition to base salary, full-time employees are eligible to participate in Keenfinity Group's (US) Health and Welfare Benefit Plans-including medical, dental, visi