The Principal Technical Architect provides technical leadership for complex data and analytics solutions across multiple programs and business domains. This role is responsible for translating business strategy into scalable, secure, and maintainable technology solutions while partnering closely with engineering, product, governance, and business teams. The architect combines strong solution design expertise with practical technical depth to ensure architecture standards, patterns, and best practices are successfully implemented in delivery.
Technical Responsibilities:
• Define solution architecture direction, standards, and reference patterns for complex data and analytics initiatives while ensuring alignment with enterprise architecture principles.
• Own solution design artifacts including architecture diagrams, technical design documents, integration blueprints and data modeling.
• Define reference architectures, design patterns, and reusable components for data ingestion, storage, transformation, and consumption.
• Develop proof-of-concepts, reference implementations, and reusable implementation patterns that accelerate delivery and reduce technical debt across programs.
• Evaluate emerging technologies, tools, and platforms, and provide recommendations on fit-for-purpose adoption.
• Conduct architecture, design and code reviews, and technical quality assessments to ensure solutions meet scalability, performance, security, reliability, and supportability requirements.
• Partner with Data Governance and Security teams to embed data privacy, access control, and compliance requirements into solution designs.
• Lead proof-of-concepts and technical evaluations for new platforms, frameworks, advanced analytics capabilities, and architectural approaches to determine business applicability and architectural fit.
• Partner with engineering teams to resolve complex performance, quality, scalability, reliability, integration, and operational challenges.
• Provide technical mentorship to engineers and architects through active engagement in solution design, implementation planning, and delivery reviews.
• Create, maintain and represent architecture documentation, standards, and decision records for the Data and Analytics vertical.
• Partner with Product Management, platform teams and business stakeholders to translate strategic requirements into practical technical designs, delivery roadmaps, and phased implementation plans.
Knowledge and Capabilities:
• Deep expertise in enterprise data architecture, including data warehousing, Lakehouse, and real-time streaming architecture.
• Strong understanding of cloud platforms, modern data platform ecosystems, and implementation practices across data engineering, integration, analytics, and operational support.
• Ability to design solutions that balance scalability, performance, cost, and maintainability.
• Ability to balance strategic architecture considerations with practical implementation requirements, delivery constraints, and engineering trade-offs.
• Strong analytical and problem-solving skills applied to ambiguous, cross-functional technical challenges.
• Effectively communicates architectural decisions and trade-offs to technical and executive audiences.
• Builds credibility and influence across engineering, product, and governance teams without direct authority.
• Proven capability to influence technical decisions through expertise, collaboration, and hands-on technical leadership rather than formal authority.
• Stays current with industry trends and emerging technologies relevant to data and analytics architecture.
Preferred Skills / Engineering tools:
• Experience with modern cloud data platforms and Lakehouse technologies, including Azure Data Services, Databricks Lakehouse, Delta Lake, Delta Live Tables, Unity Catalog, or equivalent enterprise analytics platforms.
• Strong proficiency in SQL, Python, and modern data engineering concepts, including ETL/ELT, CDC, dimensional modeling, star-schema design, medallion architecture, distributed data processing, and workload performance optimization.
• Experience with enterprise integration patterns and ERP ecosystems such as oracle, JDE, SFDC, SAP S/4HANA, including commercial, manufacturing, supply chain, and finance data domains (modern as well as legacy).
• Experience designing governed data products across bronze, silver, and gold layers, with clear lineage, data quality controls, reconciliation expectations, and consumption-ready semantic layers.
• Experience designing scalable analytics, reporting, semantic model, and business intelligence architectures, including Power BI semantic models, DAX, row-level security, refresh patterns, and performance optimization.
• Strong knowledge of API-based and event-driven integration architectures (Kafka, REST, GraphQL).
• Experience with infrastructure-as-code, CI/CD, observability, performance optimization, and DevOps practices in cloud-native data platforms.
• Familiarity with data governance, metadata management, and master data management (MDM) frameworks.
• Experience presenting architecture recommendations, solution trade-offs, and implementation approaches to senior technical and business stakeholders.
10+ years of experience in data engineering, solution architecture, or technical design roles, including significant architecture ownership.
• Demonstrated experience architecting large-scale, enterprise data platforms and integration solutions.
• Strong hands-on background in cloud data platforms, data modeling, modern data architecture patterns, Databricks or equivalent Lakehouse platforms, and engineering practices, with demonstrated ability to guide implementation teams through complex technical challenges.
• Proven ability to influence technical direction across multiple teams without direct management authority.
• Excellent written and verbal communication skills, including experience presenting to senior stakeholders.
Travel Percentage: 10%
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