Enterprise Data Architect

Role: Enterprise Data Architect

Location: Dallas, Pittsburgh, Cleveland

Experience: 12+ years

Duration: Full time

We are seeking an Enterprise Data Consultant to support the design, delivery, and optimization of large-scale data engineering, analytics, and AI-enabled solutions across the enterprise.

This role partners closely with business, technology, and architecture teams to translate complex data requirements into scalable, secure, and compliant solutions.

Key Responsibilities:

Enterprise Data Analysis & Solution Delivery

• 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.