EA Architect - Vice President

Sumitomo Mitsui Banking Corporation•Charlotte, NC

About The Position

The Solution Architect - Vice President serves as the primary architecture lead for Databricks-based data, analytics, reporting, and AI solutions. This role is responsible for defining architecture standards, integration patterns, technical requirements, and implementation guidance that enable scalable, secure, and maintainable data solutions across the enterprise. Working closely with business stakeholders, data engineering teams, application development teams, infrastructure teams, cybersecurity teams, and enterprise architects, the role provides deep technical expertise and architectural guidance for complex data and integration initiatives. The successful candidate will design solution architectures, review developer implementations, establish technical standards, write detailed technical requirements, and help delivery teams overcome complex technical challenges. This is a highly technical individual contributor role requiring deep Databricks expertise, strong data engineering knowledge, and significant experience building enterprise data pipelines and integrations. The ideal candidate combines architecture skills with the practical ability to troubleshoot, optimize, review, and support solutions directly.

Requirements

8+ years of hands-on software development, data engineering, solution architecture, or systems integration experience.
Strong hands-on experience with Databricks, Apache Spark, Delta Lake, Unity Catalog, Python, and SQL.
Extensive experience designing and implementing enterprise data pipelines, integration solutions, analytics platforms, and reporting solutions.
Strong experience developing data ingestion, transformation, orchestration, data quality, and publishing frameworks.
Deep knowledge of batch processing, streaming architectures, event-driven integrations, enterprise data movement patterns, and large-scale data processing.
Strong understanding of data modeling, dimensional modeling, data warehousing, metadata management, data lineage, and data quality processes.
Experience designing and supporting data lakehouse architectures, enterprise reporting environments, operational analytics solutions, and modern data platforms.
Experience integrating Databricks with enterprise applications, APIs, cloud services, operational platforms, reporting tools, analytics platforms, and workflow systems.
Strong understanding of Databricks Workflows, Jobs, Unity Catalog, cluster optimization, workload management, performance tuning, and operational support best practices.
Experience designing and implementing ETL/ELT architectures, ingestion frameworks, transformation pipelines, and enterprise data integration solutions.
Experience creating technical specifications, source-to-target mappings, architecture designs, interface specifications, and integration documentation.
Strong experience using Jira for backlog management, sprint planning, user story development, release coordination, dependency tracking, requirements management, and technical project execution.
Experience troubleshooting complex data, integration, performance, scalability, and production support issues.
Ability to review developer code, identify architectural and design issues, recommend improvements, and assist teams in resolving technical challenges.
Strong understanding of enterprise integration patterns, API architectures, data governance, security controls, and operational support requirements.
Strong analytical, troubleshooting, and problem-solving skills.
Demonstrated ability to translate business requirements into detailed technical solutions and developer-ready implementation requirements.
Experience providing technical oversight and guidance to development teams without direct management responsibility.
Proven success supporting enterprise-scale data integration, analytics, reporting, AI, and modernization initiatives.
Strong written and verbal communication skills.
Experience reviewing solution designs, data models, pipelines, and implementation approaches to ensure alignment with enterprise standards.

Nice To Haves

Experience integrating Databricks with ServiceNow and enterprise workflow platforms.
Experience with Azure cloud services including Azure Data Factory, Azure Storage, Azure Functions, Event Hub, and related cloud-native data architectures.
Experience designing and supporting enterprise reporting, analytics, data warehousing, and operational intelligence solutions.
Experience with ETL/ELT frameworks, enterprise data pipelines, data lakehouse architectures, and large-scale data processing platforms.
Experience working with data governance, metadata management, lineage, data quality, and regulatory reporting requirements.
Experience working within financial services or other regulated environments.
Certifications are not required. Demonstrated hands-on experience designing, building, troubleshooting, optimizing, and supporting enterprise-scale Databricks and data platform solutions is preferred over certification credentials.

Responsibilities

Define architecture standards, reference patterns, and implementation guidance for Databricks-based data, analytics, reporting, and AI solutions.
Design and implement integrations between Databricks, ServiceNow, enterprise applications, cloud platforms, APIs, cybersecurity solutions, operational systems, and external data sources.
Create detailed technical specifications, source-to-target mappings, integration designs, architecture diagrams, interface specifications, and developer-ready implementation requirements.
Design and oversee enterprise data pipelines supporting ingestion, transformation, enrichment, publishing, analytics, reporting, and operational intelligence workloads.
Review solution designs to ensure alignment with enterprise architecture principles, security requirements, scalability expectations, operational support requirements, and long-term maintainability.
Troubleshoot complex data pipeline issues, integration failures, performance bottlenecks, data quality challenges, and production support issues.
Conduct design reviews and code reviews to promote engineering standards, security requirements, operational resiliency, and maintainability.
Develop reusable ingestion, transformation, publishing, and integration patterns that support scalable and efficient data delivery.
Support analytics, reporting, operational intelligence, machine learning, and AI initiatives leveraging enterprise data assets.
Evaluate new Databricks capabilities, data technologies, AI services, and supporting platform tools to improve engineering capabilities and delivery efficiency.
Translate business requirements into practical technical solutions while ensuring scalability, supportability, observability, and long-term maintainability.
Partner with business stakeholders to understand requirements and translate business objectives into detailed technical solutions.
Collaborate with internal development and data engineering teams to review designs, troubleshoot implementation challenges, and ensure successful delivery.
Work closely with infrastructure, cybersecurity, application development, and architecture teams to support enterprise integration initiatives.
Provide technical leadership during architecture reviews, design sessions, implementation planning activities, and troubleshooting efforts.
Communicate complex technical concepts effectively to both technical and non-technical audiences.
Participate in technology onboarding activities, architecture assessments, technical reviews, and governance processes.
Build effective relationships with stakeholders while helping drive successful data, analytics, reporting, and AI outcomes across multiple initiatives.
Demonstrate deep expertise in Databricks platform architecture, data engineering, analytics engineering, and enterprise data integration technologies.
Exhibit advanced proficiency with Python and SQL development for data processing, automation, integration, reporting, and analytics workloads.
Maintain expertise in Apache Spark, Delta Lake, Databricks Workflows, Unity Catalog, notebooks, jobs, cluster management, and performance optimization techniques.
Demonstrate strong knowledge of data modeling, dimensional modeling, data warehousing concepts, metadata management, and distributed data processing architectures.
Apply expertise in API design, event-driven architectures, batch and streaming integrations, and secure enterprise data exchange.
Demonstrate deep understanding of performance tuning, troubleshooting, scalability, resiliency, monitoring, observability, and operational support requirements.
Maintain awareness of emerging Databricks capabilities, AI technologies, cloud services, analytics platforms, and data engineering best practices.
Apply technical expertise to establish standards, implementation patterns, and architectural guidance that promote consistency across enterprise data solutions.