Senior Data Architect

This is a fully remote position, open to applicants in Peru.

📋 Description

• Establish and uphold enterprise data architecture standards across various data domains, including structured, semi-structured, and unstructured data, with a focus on AI/ML workloads such as feature stores, vector databases, and embedding pipelines.

• Utilize AI-assisted modeling tools to enhance the design of data models, assess architectural trade-offs, and validate designs against business requirements prior to implementation.

• Architect and oversee the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS, ensuring optimization for both analytical and AI/ML consumption patterns.

• Create standards for data ontology, taxonomy, and semantic layers that allow AI systems to accurately and consistently interpret organizational data.

• Assess emerging data architecture trends, including retrieval-augmented generation (RAG), real-time feature serving, and vector search, and formulate a roadmap for their integration within Stellus Rx.

• Develop scalable data models and ELT/ETL pipeline architectures that accommodate both traditional analytics and AI/ML model training and inference workloads.

• Leverage AI code generation tools to expedite the creation and validation of data models, transformation logic, and pipeline configurations, minimizing manual, repetitive design tasks with intelligent, AI-assisted development.

• Set standards for data partitioning, indexing, caching, and storage optimization, employing AI-driven performance analysis to continuously validate and enhance architectural decisions.

• Collaborate with Data Engineers to convert architectural designs into production-ready pipelines, offering hands-on support and AI-enhanced design reviews.

• Define and implement data governance frameworks, data quality standards, and data contracts across the enterprise, utilizing AI-powered data observability tools to automate quality monitoring and proactively identify issues.

• Ensure that data architecture complies with relevant healthcare regulations (HIPAA, SOC 2, NIST) by using AI-assisted compliance tools to continuously monitor for policy deviations and facilitate audit evidence collection.

• Develop and sustain a master data management (MDM) strategy that guarantees the consistency, accuracy, and reliability of critical data assets across systems.

• Promote data privacy and security principles within architectural design, incorporating data lineage tracking, access controls, and anonymization methods for sensitive healthcare information.

• Design data infrastructure that acts as a foundation for AI/ML initiatives, ensuring data is accessible, well-labeled, versioned, and structured to support model training, validation, and ongoing inference at scale.

• Work closely with data scientists and ML engineers to comprehend modeling requirements and translate them into data architecture decisions that alleviate friction in the AI development lifecycle.

• Utilize AI-assisted analysis to pinpoint high-value but underutilized data assets, and devise strategies to unlock their potential for analytics and AI-driven decision-making.

• Partner with Business Intelligence and Product teams to guarantee that the data architecture facilitates self-service analytics, real-time dashboards, and AI-enhanced reporting capabilities.

• Establish and maintain data architecture standards, patterns, and best practices throughout the organization; employ AI tools to generate, review, and keep documentation up to date with minimal manual effort.

• Mentor Data Engineers and Analysts, guiding them in the application of architectural standards and AI-enhanced data development practices.

• Clearly communicate architectural decisions, trade-offs, and roadmap suggestions to both technical teams and executive leadership.

• Keep abreast of emerging data technologies, AI/ML data infrastructure trends, and industry best practices; provide recommendations regarding adoption timelines and implementation strategies.

⛳️ Requirements

• Over 7 years of experience in data architecture, data engineering, or a closely related discipline.

• More than 3 years of experience in designing enterprise-scale data platforms within cloud environments, with a strong preference for AWS.

• Proven hands-on experience using AI tools to expedite data architecture design, automate data quality, or support AI/ML workloads, including specific examples.

• Extensive expertise in data modeling techniques, including dimensional modeling, data vault, and lakehouse patterns.

• In-depth knowledge of ELT/ETL pipeline architecture and workflow orchestration, such as Airflow or similar tools.

• Familiarity with cloud data platforms, including AWS Redshift, S3, Glue, and Athena or their equivalents.

• Proficient in SQL and at least one scripting language, preferably Python.

• Experience with both relational and NoSQL databases; knowledge of vector databases is an advantage.

• Strong understanding of data governance, data quality frameworks, and master data management principles.

• Acquainted with healthcare data compliance standards such as HIPAA and SOC 2.

• Exceptional communication skills, capable of conveying complex architectural concepts to both technical and non-technical audiences.

• Bachelor's or graduate degree in Computer Science, Information Systems, Statistics, or a related quantitative field.

• High proficiency in English, both written and verbal.

🏝️ Benefits

• Health insurance

• Opportunities for professional development