Enterprise Architecture Sr (Enterprise Data Architect Sr)

PNC • Strongsville, OH

21h • $80,000 - $185,150 • Onsite

About The Position

At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success. As a Enterprise Architect Sr within PNC's Enterprise Technology Architecture's organization, you will be based in Pittsburgh, PA or Strongsville, OH or Dallas, TX or Phoenix, AZ or Lakewood, CO. Position Overview An Enterprise Architect Sr position that collaborates with key data and technology stakeholders to build out the enterprise data architecture practice. The objective is to execute PNC’s data strategy by establishing data architecture models and standards that enable data observability, interchangeability, integrity, confidentiality, and availability. This senior-level role requires leadership skills and creative-thinking as well as hands-on expertise to guide teams to build out the semantic and object standards that are the foundation for data taxonomies, data models, data flows, lineage diagrams, and entity relationship models. This role operates within a TOGAF-oriented Enterprise Architecture practice that is accountable for producing and maintaining enterprise data architecture artifacts, supporting architecture governance, and guiding data related decisions from strategy through implementation. Key deliverables, in partnership with the other stakeholders are: Strategy: • Build strategies that guide business and technology leaders that meet data technologies needs • Create usable architecture artifacts that measure and demonstrate progress against a strategy • Guide stakeholders with their data priorities by developing metrics ensuring objectives are met • Measure progress against strategic data objectives and produce results analysis with follow-up • Articulate the business impact of complex data flows and data duplication decision making Architecture: • Create data reference architectures that establish data consumption standards and patterns • Identify and document data flows that form the basis for optimizing data lineage and retention • Produce data architecture analysis with supporting artifacts for technology recommendations • Develop architecture patterns that drive common data flows and standardized integration • Identify, assess, and resolve complex data definitions and semantic structures inconsistencies Modeling: • Lead the development of data modeling standards, best practices, and guidelines • Design data entity and flow models that enable operational reporting, analytics, and modeling • Lead the development, standardization, and evolution of canonical and conceptual data models • Maintain comprehensive inventory of contextual, conceptual, logical and physical data models • Ensure data models comply with data architecture policies and standards with validation checks Classification: • Oversee metadata, master data, and reference data management to ensure data consistency • Design ontologies and semantic models for driving the adoption of data mesh and virtualization • Drive alignment of data ontologies and data semantics that support regulatory requirements • Serve as the principal authority for metadata standards collaborating with Data Owners/Stewards • Establish enterprise standards for ontology modeling, naming conventions, and data taxonomies Collaboration: • Work with enterprise and solution architects to implement data observability capabilities • Work closely with data architects and guide how data products are aligned to the data standards • Partner with architecture teams to align solution architectures with data architecture standards • Champion the definition and adoption of semantic layers that drive remediation best practices • Provide thought leadership and mentorship to translate complex data strategies and concepts Governance: • Measure progress against objectives with follow-up when results are or are not achieved • Ensure adherence to best practices, data principles, and semantic data governance • Establish and govern enterprise standards for ontology modeling and naming conventions • Define auditability and traceability standards for financial and operational data with metrics • Establish, document, and maintain governance practices for data modeling and architecture Some key desired skills: • Enterprise systems thinking & broad understanding of managing to a capability model • Strategic thinking and planning with a proactive approach to anticipate their needs and objectives • Experience on large strategic transformation programs • Experience working in financial services organizations with AI and Cloud technologies • Personal skills: Communication, Relationship Building, Collaboration, Influencing • Certifications in DCAM, CDMP, DMC, TOGAF, or SAFe are desirable but not required Individual Contributor Qualifications • TOGAF certification or equivalent enterprise architecture experience • Experience in large, complex enterprise environments • Familiarity with both operational and analytical data platforms

Requirements

• Enterprise systems thinking & broad understanding of managing to a capability model
• Strategic thinking and planning with a proactive approach to anticipate their needs and objectives
• Experience on large strategic transformation programs
• Experience working in financial services organizations with AI and Cloud technologies
• Personal skills: Communication, Relationship Building, Collaboration, Influencing
• TOGAF certification or equivalent enterprise architecture experience
• Experience in large, complex enterprise environments
• Familiarity with both operational and analytical data platforms
• Conceptual, logical, and physical data modeling
• Data lifecycle management and data integration patterns
• Metadata, lineage, and data quality concepts
• Data security, privacy, and classification principles
• Master and reference data concepts
• Successful candidates must demonstrate appropriate knowledge, skills, and abilities for a role.
• Roles at this level typically require a university / college degree.
• Industry relevant experience is typically 8+ years.
• Specific certifications are often required.

Nice To Haves

• Certifications in DCAM, CDMP, DMC, TOGAF, or SAFe are desirable but not required
• Hands-on experience executing Data Architecture within a TOGAF or TOGAF-like EA framework
• Strong understanding of baseline/target architecture, gap analysis, and transition planning
• Experience working within formal architecture governance processes
• Ability to translate business needs into data architecture outcomes
• Strong written and verbal communication skills
• Proven ability to influence decisions without direct authority
• Higher level education such as a Masters degree, PhD, or certifications is desirable.
• Competitive Advantages
• Customer Solutions
• Design
• Enterprise Architecture Framework
• Enterprise Data
• Enterprise Data Architecture
• Machine Learning (ML)
• Risk Assessments
• Technical Knowledge
• Decision Making and Critical Thinking
• Emerging Technologies
• Enterprise IT Architecture
• Industry Knowledge
• IT Architecture
• Organizational Leadership
• Service Oriented Architecture Technologies
• Strategic Thinking
• The Open Group Architecture Framework (TOGAF)

Responsibilities

• Build strategies that guide business and technology leaders that meet data technologies needs
• Create usable architecture artifacts that measure and demonstrate progress against a strategy
• Guide stakeholders with their data priorities by developing metrics ensuring objectives are met
• Measure progress against strategic data objectives and produce results analysis with follow-up
• Articulate the business impact of complex data flows and data duplication decision making
• Create data reference architectures that establish data consumption standards and patterns
• Identify and document data flows that form the basis for optimizing data lineage and retention
• Produce data architecture analysis with supporting artifacts for technology recommendations
• Develop architecture patterns that drive common data flows and standardized integration
• Identify, assess, and resolve complex data definitions and semantic structures inconsistencies
• Lead the development of data modeling standards, best practices, and guidelines
• Design data entity and flow models that enable operational reporting, analytics, and modeling
• Lead the development, standardization, and evolution of canonical and conceptual data models
• Maintain comprehensive inventory of contextual, conceptual, logical and physical data models
• Ensure data models comply with data architecture policies and standards with validation checks
• Oversee metadata, master data, and reference data management to ensure data consistency
• Design ontologies and semantic models for driving the adoption of data mesh and virtualization
• Drive alignment of data ontologies and data semantics that support regulatory requirements
• Serve as the principal authority for metadata standards collaborating with Data Owners/Stewards
• Establish enterprise standards for ontology modeling, naming conventions, and data taxonomies
• Work with enterprise and solution architects to implement data observability capabilities
• Work closely with data architects and guide how data products are aligned to the data standards
• Partner with architecture teams to align solution architectures with data architecture standards
• Champion the definition and adoption of semantic layers that drive remediation best practices
• Provide thought leadership and mentorship to translate complex data strategies and concepts
• Measure progress against objectives with follow-up when results are or are not achieved
• Ensure adherence to best practices, data principles, and semantic data governance
• Establish and govern enterprise standards for ontology modeling and naming conventions
• Define auditability and traceability standards for financial and operational data with metrics
• Establish, document, and maintain governance practices for data modeling and architecture

Benefits

• PNC offers a comprehensive range of benefits to help meet your needs now and in the future.
• Depending on your eligibility, options for full-time employees include: medical/prescription drug coverage (with a Health Savings Account feature), dental and vision options; employee and spouse/child life insurance; short and long-term disability protection; 401(k) with PNC match, pension and stock purchase plans; dependent care reimbursement account; back-up child/elder care; adoption, surrogacy, and doula reimbursement; educational assistance, including select programs fully paid; a robust wellness program with financial incentives.
• In addition, PNC generally provides the following paid time off, depending on your eligibility: maternity and/or parental leave; up to 11 paid holidays each year; 9 occasional absence days each year, unless otherwise required by law; between 15 to 25 vacation days each year, depending on career level; and years of service.
• PNC’s total rewards package includes things like time off, benefits, learning and career development, wellness programs, recognition and much more.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level