Principal Duties & Responsibilities
• Architect end to end data solutions and provide implementation oversight in alignment with enterprise architecture standards.
• Rapidly assess the current data landscape and identify gaps, risks, and opportunities to accelerate modernization.
• Deliver actionable architecture artifacts—including data models, integration patterns, and platform designs—under compressed timelines.
• Enhance foundational data capabilities such as ingestion pipelines, domain models, metadata frameworks, and data quality structures.
• Define, document, and promote scalable architecture patterns for immediate adoption by engineering teams.
• Partner closely with architects, engineers, and business stakeholders to translate requirements into actionable designs that fit enterprise standards.
• Provide hands on architectural leadership for high priority initiatives, ensuring solutions are modern, scalable, and aligned with enterprise principles while enabling speed.
• Introduce external best practices across cloud, data mesh, event driven architectures, and AI/ML enabled solutions.
• Advise on platform/tooling selections and modernization pathways grounded in real industry experience and time to value.
• Produce clear documentation, decision records, and transition materials to enable seamless handoff to full time teams.
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Knowledge & Skills
• Broad understanding of IT systems and how business processes interact with applications, databases, storage platforms, security, and networks.
• Deep experience with relational and non-relational databases, including but not limited to DynamoDB and Neptune.
• Strong experience designing and implementing Data APIs using cloud technologies; MuleSoft experience is a plus.
• Expertise in data movement, data quality enforcement, and cloud based data processing.
• Prior experience architecting fault tolerant, self-healing, highly scalable, or multi-tenant data systems.
• Strong understanding of data management patterns and best practices.
• Familiarity with enterprise and service design patterns, including practical application of Data Mesh and Data Product paradigms.
• Ability to design flexible systems and adopt new or emerging technologies as appropriate.
• Comfortable operating with minimal supervision in a complex hybrid environment.
• Brings industry knowledge from working across multiple organizations or modern data intensive transformations.
• Recommends best practices based on cloud adoption patterns, analytics needs, and AI/ML readiness.
• Able to challenge existing approaches and introduce new, reusable patterns.
• Capable of producing high clarity architecture artifacts (decision records, diagrams, standards) that can be adopted immediately.
• Understanding of modern data ingestion and transformation patterns (streaming, batch, CDC).
• Familiarity with data pipeline frameworks, orchestration tools, and platform services (Spark, Kafka, Snowflake, Databricks, Glue, Airflow, etc.
• Ability to quickly assess complex data ecosystems and recommend modernization paths.
• Strong understanding of cloud-based data architectures (AWS), Data mesh patterns, and modern data stack components.
