Solutions Architect

Job Title: Solutions Architect Location: Charlotte, NC/ NY/NJ Experience: 15–20 Years Job Summary We are seeking an accomplished Principal Solutions Architect – Data, Analytics & Generative AI to lead strategic engagements and architect next-generation data and AI platforms. This is a highly visible role responsible for partnering with CxOs, and other senior executives to define technology roadmaps, shape data transformation strategies, and deliver business outcomes through modern data, analytics, and AI solutions. The ideal candidate combines deep technical expertise with executive communication. Ideal candidate should have extensive experience designing enterprise-scale data platforms, modern data engineering ecosystems, AI/ML and Generative AI solutions, and cloud-native architectures while serving as a trusted advisor to executive stakeholders. This role requires the ability to bridge business strategy with technology execution, lead complex solutioning initiatives, drive innovation, and mentor technical teams. Key Responsibilities Lead executive-level strategy discussions, technology workshops, and architecture vision sessions. Translate business objectives into enterprise technology strategies and transformational roadmaps. Build long-term relationships with senior customer stakeholders and influence strategic technology decisions. Define enterprise-wide data, analytics, and AI architecture aligned with business goals. Architect end-to-end modern data platforms leveraging cloud-native services and scalable design principles. Lead modernization of legacy data platforms and migration to cloud environments. Define enterprise integration patterns including APIs, event-driven architectures, streaming, and hybrid integration models. Establish architectural standards, reusable patterns, and reference architectures across the organization. Define enterprise data models, metadata strategies, Master Data Management (MDM), data quality, and governance frameworks. Enable self-service analytics, business intelligence, and semantic data models. Design enterprise GenAI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, AI agents, knowledge management, and intelligent automation. Define secure, scalable, and responsible AI architectures including governance, model lifecycle management, prompt engineering, and AI observability. Identify high-value AI and GenAI use cases that accelerate business transformation and operational efficiency. Provide technical leadership and mentorship to architects, engineers, and delivery teams. Support customer workshops, proof of concepts (POCs), demonstrations, and technology assessments. Required Skills Data Platforms Databricks Snowflake Microsoft Fabric Azure Synapse Analytics AWS Redshift Google BigQuery Cloud Technologies Microsoft Azure Amazon Web Services (AWS) Google Cloud Platform (GCP) Data Engineering Apache Spark PySpark Delta Lake Apache Kafka Airflow DBT ETL/ELT Architecture Programming Python SQL Scala (preferred) Java (preferred) Databases SQL Server Oracle PostgreSQL MongoDB Cosmos DB Data Integration Azure Data Factory Informatica Talend Fivetran APIs Event-driven architecture AI & Analytics Machine Learning fundamentals Generative AI and Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI Agents / Agentic AI concepts and Intelligent Automation Azure OpenAI or equivalent LLM platforms Data preparation for AI workloads Vector Databases Responsible AI and AI Governance Required Experience 15–20 years of experience in Data Engineering, Data Architecture, or Enterprise Data Solutions. Extensive experience designing enterprise-scale data platforms and cloud-native architectures Strong expertise in Databricks and modern Lakehouse architecture. Experience with Azure, AWS, or GCP cloud ecosystems. Experience building large-scale batch and streaming data pipelines. Strong understanding of data modelling, dimensional modelling, and data warehouse design. Experience with enterprise data governance and security frameworks. Experience working with executive stakeholders and business leaders. Experience leading globally distributed engineering teams. Exposure to AI, GenAI, and modern analytics architectures. Preferred Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. Master's degree is preferred. Cloud certifications (Azure Solutions Architect, AWS Solutions Architect, or GCP Professional Cloud Architect). Databricks Certified Data Engineer or Databricks Certified Solutions Architect. Snowflake Certification (preferred). TOGAF or equivalent architecture certification (preferred).