Enterprise Architect Sr; Enterprise Data Architect Sr

Position: Enterprise Architect Sr (Enterprise Data Architect Sr)
Position Overview

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…