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Description The Enterprise Data Architect is a strategic and technical leader responsible for designing, governing, and evolving the enterprise data portfolio with alignment to industry-leading architecture and practices. This role ensures alignment between business goals and data capabilities by collaborating across business units, security, risk, engineering, cloud enablement, data science, and enterprise API governance teams. The architect will drive the development of scalable, secure, and intelligent data platforms, enabling advanced analytics, AI / ML, and generative AI use cases across the organization. Additional Responsibilities : Enterprise Data Architecture & Strategy - Define and evolve the enterprise-wide data architecture, including data lakes, data warehouses, data mesh, and data fabric. Develop and maintain a forward-looking data strategy aligned with business and digital transformation goals. Lead the design of cloud-native, API-driven, and event-driven data platforms. Developing and maintaining the enterprise data architecture standards, including data modeling practices, data management standards, and industry best practices for banking and financial services. Designing data integration solutions and processes, including ETL (Extract, Transform, Load), to ensure seamless data flows across the organization. Collaborating with cybersecurity architecture, cloud center-of-excellence, data engineering, and business stakeholders to deliver data solutions. Staying current with emerging data technologies and platforms (e.g., cloud-based analytics, data lakes, data warehousing), evaluating and recommending solutions that meet architectural principles. Working closely with business stakeholders, product owners, data scientists, engineers, and API governance teams to co-create data-driven solutions. Translating business requirements into scalable and secure data solutions. Providing architectural oversight for enterprise data initiatives and modernization efforts. AI / ML & Generative AI Enablement - Partnering with data science teams to support AI / ML data pipelines, feature engineering, model training & inference, and MLOps. Architecting platforms for generative AI, LLMs, NLP, computer vision, and responsible AI. Ensuring AI model governance and ethical AI use. Preferred Skills / Experience : Enterprise Data Architecture: Proven ability to design and implement scalable, secure, and high-performance data architectures across cloud and hybrid environments. Data Strategy & Governance: Expertise in defining data strategies, governance frameworks, and compliance policies to ensure data quality, integrity, and regulatory adherence. Data Modeling & Warehousing: Strong experience in conceptual, logical, and physical data modeling, as well as designing and optimizing data warehouses and data lakes. Modern Data Paradigms: Hands-on knowledge of Data Mesh, Data Fabric, and API-driven and event-driven architectures. Data Integration & Pipelines: Proficiency in building and managing ETL / ELT pipelines, streaming data flows, and batch processing systems at scale. DataOps & Automation: Familiarity with DataOps practices for CI / CD in data engineering, including monitoring, testing, and deployment automation. Security & Compliance: Deep understanding of data security, privacy, lineage, and compliance frameworks (e.g., GDPR, HIPAA). Cloud Platforms: Expertise in AWS (Glue, Redshift, Athena, SageMaker, EventBridge, RDS, Aurora, DynamoDB, Bedrock), Azure (Synapse, Data Factory), and GCP (BigQuery, Dataflow). Data Platforms & Warehousing: Hands-on experience with Snowflake, Databricks, Delta Lake, Amazon Kinesis, and Amazon Athena. Streaming & Messaging: Proficiency with Apache Kafka, Apache Flink, and Amazon Kinesis for real-time data processing. Data Engineering Tools: Skilled in Informatica, Talend, and dbt (Data Build Tool) for data transformation and orchestration. Data Catalog & Governance Tools: Familiarity with Collibra, Alation, Monte Carlo, and BigID for metadata and data quality management. Query Engines & Databases: Experience with Presto, Trino, SQL Server, PostgreSQL, Oracle Exadata, and NoSQL databases (MongoDB, Cassandra, DynamoDB). AI / ML Frameworks: Proficient in TensorFlow, PyTorch, Scikit-Learn, Hugging Face, and OpenAI API. Vector Databases & APIs: Knowledge of Weaviate, Pinecone, FAISS, and GraphQL for vector search and API-driven data access. Machine Learning & AI: Experience designing and supporting ML pipelines, including model training, inference, and deployment. Generative AI & LLMs: Knowledge of generative AI, large language models (LLMs), and their enterprise use cases. Advanced AI Techniques: Familiarity with deep learning, neural networks, NLP, computer vision, and responsible AI practices. MLOps & Governance: Competence in MLOps, feature engineering, and AI model governance to ensure scalable and ethical AI deployment. This position may be filled at a higher level depending on the candidate's qualifications and relevant experience. This position is intended to be onsite, now or in the near future. Associates will have regular work hours, including full days in the office three or more days a week. The manager will set the work schedule, including in-office expectations. Regions will not provide relocation assistance; relocation would be at your expense. The available locations are: Birmingham, AL Nashville, TN Atlanta, GA Charlotte, NC Dallas, TX Denver, CO Houston, TX Orlando, FL Salt Lake City, UT Tampa, FL Position Type: Full-time. Compensation is based on market ranges and individual factors such as experience, skills, and performance. The target salary range is $120,156.85 - $156,060.00 USD. Incentive plans and benefits include: Paid Vacation / Sick Time 401K with Company Match Medical, Dental, and Vision Benefits Disability Benefits Health Savings Account Flexible Spending Account Life Insurance Parental Leave Employee Assistance Program Volunteer Program Benefits are subject to change. For more information, visit our benefits page. Applications are accepted electronically for at least five business days from posting. Higher-volume positions may remain open longer.

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