Enterprise Data Architect

Location: Dallas, Pittsburgh, Cleveland

Experience: 12+ years

Duration: Full timeKey ResponsibilitiesPartner with business and technology stakeholders to analyze enterprise data requirements and translate them into scalable data engineering and analytics solutionsDesign, build, and support end-to-end data pipelines, including data ingestion, preprocessing, normalization, transformation, quality checks, and loading across complex data ecosystemsLead and contribute to ETL/ELT development using technologies such as Spark, Hadoop, Hive, Kafka, Python, and Scala, ensuring performance, reliability, and data accuracyData Platforms & ArchitectureWork with distributed data platforms including HDFS, HBase, Sqoop, Flume, and MapReduce, supporting both batch and real-time processing use casesApply strong data modeling and data design principles to support analytics, reporting, regulatory, and operational needsCollaborate with enterprise architects on logical and physical data models aligned with PNC standardsData Quality, Governance & ComplianceSupport and implement data quality frameworks, including profiling, validation rules, reconciliation, and monitoring to ensure trusted and compliant dataCollaborate with cross-functional teams to ensure solutions align with enterprise architecture, security, governance, and regulatory requirementsCloud, Analytics & AI EnablementContribute to cloud-based data solutions, particularly on AWS, supporting data processing, analytics, and ML workloadsCollaborate with data scientists and ML engineers to enable machine learning and AI use cases, including feature engineering, data preparation, and pipeline integrationSupport development and deployment of ML and AI systems, including exposure to LLM-based solutions, feature stores, and ML lifecycle management toolsMLOps & Agile DeliveryParticipate in or support MLOps practices, including model deployment, monitoring, retraining pipelines, and integration with platforms such as SageMaker, MLflow, Kubeflow, or similar toolsWork in Agile delivery environments, actively participating in sprint planning, stand-ups, reviews, and retrospectives using tools such as JiraStakeholder Engagement & ConsultingServe as a client-facing consultant, coordinating across the SDLC and communicating technical concepts clearly to both technical and non-technical stakeholdersContribute to solutioning, estimations, POCs, and client proposals, helping shape data, analytics, and AI modernization initiativesPeople & Capability DevelopmentMentor junior team members, support onboarding, and promote best practices in data engineering, analytics, and platform designFoster collaboration across teams to support continuous improvement and delivery excellenceQualifications & Experience 12+ years of experience in data engineering, data analytics, or enterprise data consultingStrong hands‑on experience with big data and distributed data platformsProficiency in Python, with experience in streaming and real‑time data processingSolid understanding of data modeling, ETL/ELT design, and data quality practicesExperience supporting cloud‑based data platforms, preferably AWSExposure to machine learning, AI, and MLOps concepts preferredExperience working in Agile/Scrum environmentsStrong communication and consulting skills with experience working in client‑facing rolesBachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field