Enterprise Data Architect

Role: Enterprise Data Architect Location: Dallas, Pittsburgh, Cleveland Experience: 12+ years Duration: Full time Key Responsibilities Partner with business and technology stakeholders to analyze enterprise data requirements and translate them into scalable data engineering and analytics solutions Design, build, and support end-to-end data pipelines, including data ingestion, preprocessing, normalization, transformation, quality checks, and loading across complex data ecosystems Lead and contribute to ETL/ELT development using technologies such as Spark, Hadoop, Hive, Kafka, Python, and Scala, ensuring performance, reliability, and data accuracy Data Platforms & Architecture Work with distributed data platforms including HDFS, HBase, Sqoop, Flume, and MapReduce, supporting both batch and real-time processing use cases Apply strong data modeling and data design principles to support analytics, reporting, regulatory, and operational needs Collaborate with enterprise architects on logical and physical data models aligned with PNC standards Data Quality, Governance & Compliance Support and implement data quality frameworks, including profiling, validation rules, reconciliation, and monitoring to ensure trusted and compliant data Collaborate with cross-functional teams to ensure solutions align with enterprise architecture, security, governance, and regulatory requirements Cloud, Analytics & AI Enablement Contribute to cloud-based data solutions, particularly on AWS, supporting data processing, analytics, and ML workloads Collaborate with data scientists and ML engineers to enable machine learning and AI use cases, including feature engineering, data preparation, and pipeline integration Support development and deployment of ML and AI systems, including exposure to LLM-based solutions, feature stores, and ML lifecycle management tools MLOps & Agile Delivery Participate in or support MLOps practices, including model deployment, monitoring, retraining pipelines, and integration with platforms such as SageMaker, MLflow, Kubeflow, or similar tools Work in Agile delivery environments, actively participating in sprint planning, stand-ups, reviews, and retrospectives using tools such as Jira Stakeholder Engagement & Consulting Serve as a client-facing consultant, coordinating across the SDLC and communicating technical concepts clearly to both technical and non-technical stakeholders Contribute to solutioning, estimations, POCs, and client proposals, helping shape data, analytics, and AI modernization initiatives People & Capability Development Mentor junior team members, support onboarding, and promote best practices in data engineering, analytics, and platform design Foster collaboration across teams to support continuous improvement and delivery excellence Qualifications & Experience 12+ years of experience in data engineering, data analytics, or enterprise data consulting Strong hands-on experience with big data and distributed data platforms Proficiency in Python, with experience in streaming and real-time data processing Solid understanding of data modeling, ETL/ELT design, and data quality practices Experience supporting cloud-based data platforms, preferably AWS Exposure to machine learning, AI, and MLOps concepts preferred Experience working in Agile/Scrum environments Strong communication and consulting skills with experience working in client-facing roles Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field J-18808-Ljbffr