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 Map Reduce, 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 Sage Maker, 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

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