Data Architect (Hadoop/ML/AI/ETL)

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 Data Architect within PNC's DNA Enterprise Warehouse organization, you will be based in Pittsburgh, PA or Strongsville, OH or Dallas, TX. We are seeking an experienced Data Architect to lead the architecture, and control frameworks for D&A technology, on-premises data ecosystem. In this role, you will design robust data movement controls, reconciliation pipelines, and semantic abstraction layers that connect high-throughput relational data warehouses, legacy Hadoop infrastructure, and modern on-premises Lakehouse environments. You will be responsible for defining standards that guarantee high availability, strict enterprise security, and accurate, audited data movement across networks and private storage cluster.

1. On-Premises Data Movement & Control Architecture 1. Control & Reconciliation frameworks: Architect automated, enterprise-wide data validation mechanisms (source-to-target row count verification, checksum validation, automated threshold alerting and schema evolution tracking) across internal networks. 2. Dual-Orchestration Operations: Define standards bridging enterprise job scheduling (Control-M for batch/legacy workflows) with programmatic DAG orchestration (Apache Airflow deployed on-perm). 2. Lakehouse, Catalog & Graph Architecture 1. Rest Catalog Governance (Apache Polaris): Architect and maintain Apache Polaris as the centralized, open-source REST catalog for Apache Iceberg tables. Enforce multi-engine catalog governance, role-based access control (RBAC), and credential scoping across engines. 2. Query Federation (Trino): Implement and tune Trino across local bare-metal or private Kubernetes clusters to federate SQL queries seamlessly across relational databases, Hadoop and Iceberg storage. 3. Graph Databases & Knowledge Graphs: Architect property or RDF graph database integrations (Graph DB) to capture complex entity relationships, data lineage, and semantic ontologies. 3. Auditability & Lineage: Maintain complete operational metadata, structural tracking, and end-to-end data lineage across all internal data movement paths for strict regulatory compliance. • Experience in data architecture and enterprise data management within large-scale, on-premises data center environments. • Proven track record designing enterprise-grade data control frameworks, reconciliation patterns, audit mechanisms, and schema evolution strategies for high-throughput environments. • Deep conceptual and architectural understanding of on-premises Hadoop ecosystems (HDFS, YARN, Hive, Spark, Kerberos) and strategies for modernizing legacy workloads.

1. Agentic AI Strategy in Data Management: Exposure to or conceptual understanding of Agentic AI frameworks- specifically how autonomous agents can be leveraged for automated pipeline observability, intelligent metadata tagging, self-healing quality checks, and context-aware schema drift mitigation. 2. In this position, you will: • Provide thought leadership for the control architecture evolution within the DnA Data Ops ecosystem. • Work with Architectural Leadership to drive out the strategic roadmap for all applications and ensure appropriate architectural and technical items are suitably prioritized. • Work with Product Owners to ensure prioritization and delivery of business priorities in a timely manner via the monthly releases. • participate in business requirements gathering sessions and understand client requirements. • Create, present, and get signoffs for design documents. • Support the enterprise architecture and perform proof of concepts for