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Job Title: Senior Data Solution Architect Job Summary: The Senior Data Solution Architect is a visionary and technical leader responsible for designing and guiding enterprise-scale data solutions. Leveraging 20+ years of experience, this individual works closely with business and IT stakeholders to deliver scalable, secure, and high-performing data architectures that support strategic goals, data-driven innovation, and digital transformation. This role encompasses solution design, platform modernization, cloud data architecture, and deep integration with enterprise systems. Key Responsibilities: Solution Architecture & Design Lead the end-of-the-end architecture of complex data solutions across domains including analytics, AI/ML, MDM, and real-time processing. Design robust, scalable, and future-ready data architectures using modern technologies (e.g., cloud data platforms, streaming, NoSQL, graph databases). Deliver solutions that balance performance, scalability, security, and cost-efficiency. Enterprise Data Integration Architect seamless data integration across legacy systems, SaaS platforms, IoT, APIs, and third-party data sources. Define and implement enterprise-wide ETL/ELT strategies using tools like Informatica, Talend, DBT, Azure Data Factory, or AWS Glue. Support real-time and event-driven architecture with tools such as Kafka, Spark Streaming, or Flink. Cloud Data Platforms & Infrastructure Design cloud-native data solutions on AWS, Azure, or GCP (e.g., Redshift, Snowflake, BigQuery, Databricks, Synapse). Lead cloud migration strategies from legacy systems to modern, cloud-based data architectures. Define standards for cloud data governance, cost management, and performance optimization. Data Governance, Security & Compliance Partner with governance teams to enforce enterprise data governance frameworks. Ensure solutions comply with regulations such as GDPR, HIPAA, CCPA, and industry-specific mandates. Embed security and privacy by design in data architectures (encryption, role-based access, masking, etc.). Technical Leadership & Stakeholder Engagement Serve as a technical advisor to CIOs, CDOs, and senior business executives on data strategy and platform decisions. Mentor architecture and engineering teams; provide guidance on solution patterns and best practices. Facilitate architecture reviews, proof-of-concepts (POCs), and technology evaluations. Innovation & Continuous Improvement Stay abreast of emerging trends in data engineering, AI, data mesh, data fabric, and edge computing. Evaluate and introduce innovative tools and patterns (e.g., serverless data pipelines, federated data access). Drive architectural modernization, legacy decommissioning, and platform simplification. Qualifications: Education: Bachelor’s degree in computer science, Engineering, Information Systems, or related field; Master’s or MBA preferred. Experience: 20+ years in IT with at least 10 years in data architecture or solution architecture roles. Demonstrated experience in large-scale, complex data platform architecture and enterprise transformations. Deep experience with multiple database technologies (SQL, NoSQL, columnar, time series). Strong programming/scripting background (e.g., Python, Scala, Java, SQL). Proven experience architecting on at least one major cloud provider (AWS, Azure, GCP). Familiarity with DevOps, CI/CD, and DataOps practices. Preferred Certifications: AWS/Azure/GCP Solution Architect (Professional level preferred) TOGAF or Zachman Framework Certification Snowflake/Databricks Certified Architect CDMP (Certified Data Management Professional) or DGSP Key Competencies: Strategic and conceptual thinking with the ability to translate business needs into technical solutions. Exceptional communication, presentation, and negotiation skills. Leadership in cross-functional teams and matrix environments. Deep understanding of business processes, data monetization, and digital strategy. Success Indicators: Delivery of transformative data platforms that enhance analytics and decision-making. Improved data integration, quality, and access across the enterprise. Successful migration to cloud-native or hybrid architectures. Reduction of technical debt and legacy system dependencies. Increased reuse of solution patterns, accelerators, and frameworks. Show more Show less
