Job Description
Defines and governs enterprise data architecture supporting business intelligence, applications, automation, and artificial intelligence. Establishes scalable, secure, and cost-effective patterns for data ingestion, integration, storage, modeling, governance, metadata, lineage, and consumption. Evaluates source systems, data flows, platforms, and technical requirements and translates them into target-state architectures, standards, and roadmaps. Provides architectural direction so data is trusted, accessible, reusable, compliant, and ready for future business needs.
- Define enterprise data architecture, target-state designs, standards, and roadmaps across ingestion, integration, storage, transformation, analytics, application, and AI layers.
- Establish data models, canonical definitions, metadata, lineage, master/reference data, and semantic patterns that promote consistency and reuse.
- Define architectures for data pipelines, APIs, warehouses/lakehouses, cloud platforms, and structured and unstructured data.
- Embed data governance, quality, security, privacy, access, retention, resiliency, and regulatory requirements into architectural standards.
- Evaluate platforms and data sources, conduct design reviews, document decisions, and guide teams on scalability, performance, and cost tradeoffs.
Job Qualifications:
- Can work in a Hybrid environment and in a Night-shift schedule
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, or related field; equivalent relevant experience may be considered.
- Strong leadership and management skills
- At least 5 years of experience in data architecture, data engineering, enterprise data platforms, or related disciplines, including experience designing enterprise-scale data solutions.
- Knowledge of enterprise data architecture, data modeling, integration patterns, metadata and lineage, data governance, security, cloud data architecture, and analytical/semantic design.
- Experience with cloud data platforms, data warehouses/lakehouses, SQL, data modeling tools, ETL/ELT and orchestration technologies, APIs, and BI/analytics environments.
- Familiarity with programming, automation, and AI/ML data requirements preferred.
- Strong architecture, problem-solving, communication, and documentation skills; ability to establish standards and influence technical and business stakeholders.
