Solution Architect - Data Analytics & Cloud - Remote

Solution Planning And Delivery Solution Planning: Partners with Enterprise Architecture to develop strategies and standards for Data Analytics. Researches and understands technical requirements, trends, and strategies for Data Analytics solutions implementation. Assesses all project information to understand the scope, the current IT and business environment, objectives and priorities. Defines and documents the proposed, high-level solution, to create Data, BI, and Analytics solutions. Analyzes technical risks and advises on risk mitigation strategies. Works with the Delivery Manager, Product Owner, Business stakeholders, Engineering team and Leadership to capture key architectural requirements and assist in developing a viable plan. Solution Delivery: Applies technical knowledge of enterprise-scale architecture solutions to meet business and information technology (IT) requirements and resolve identified technical constraints. shaping and enhancing customers' requirements. Responsible for the technical content (architecture and design), integrity and quality of the solution. Accountable for the delivery of the solution as specified (by requirements and architecture). Evaluates non-functional requirements for testability and technical acceptance criteria. Addresses compliance requirements such as Payment Card Industry (PCI), Health Insurance Portability and Accountability Act (HIPAA), Personally Identifiable Information (PII), and Sarbanes-Oxley (SOX). Defines and documents the actions needed to close architecture gaps in support of business objectives. Participates in reviews of the designs, prototypes and other technical deliverables to ensure they fulfill requirements and meet standards. Required 10+ years' of professional Information Technology experience in solutioning, designing, development, and delivering Data Analytics Solutions.10+ years combined experience: Analytics, Data Science, Data Engineering, Business Intelligence, Software Development, Program Management, technical/engineering role(s) with a data focus. Technical background in large scale data warehousing, data lakes, distributed data processing, algorithmic processing (AI/ML workloads) and advanced analytics concepts. Strong technical proficiency in Azure cloud services and advanced working knowledge of Reporting, Data Visualization development (PowerBI Preferred).Experience with orchestration and administration of enterprise data warehouse/data lake and data application environments (e.g., Synapse, PowerBI, Databricks, and Tableau etc.).Strong capability to manipulate and analyze complex, abundant data from various sources. Exceptional written and verbal communication to present ideas and influence outcomes through partnership with business leaders, engineering teams and stakeholders. Effective communication skills with technical team members as well as business partners. Able to distill complex ideas into straightforward language. Proven skills in leadership, relationship building, negotiation, collaboration, advocacy, governance, and consensus building. Knowledge of IT Application architecture, design methodologies across multiple platforms. Thorough understanding of and experience with multiple SDLC methods such as waterfall, SCRUM, and/or Agile. Ability to make architecture changes in a dynamic agile environment. Recommended Breadth of technical experience and knowledge for Data & Analytics Architects. Software development practices like DevOps and CI/CD tool chains (i.e., Jenkins, ADO, and GitHub).Certification in one or more of the following technologies preferred: Cloud, Database, Data Engineering, Data Analytics, BI, Data Science, Machine Learning, and Artificial Intelligence. Expertise in two or more data estate workloads like Teradata, Oracle Exadata, Synapse, Cloudera/Hortonworks, Apache Spark, Snowflake, AWS (Redshift, Glue), Google (BigQuery), etc. Solution Architecture Professional Certification (e.g., IASA CITA-F, CITA-A or higher).Experience in using TOGAF as an architecture design methodology.