Senior Data Information Architect

As a Senior Data Information Architect at Qvelo, you will play a pivotal role in designing and implementing robust data architectures that support advanced data analytics, high-performance computing (HPC) environments, and AI-driven workloads. You will be responsible for defining data strategies, developing data governance frameworks, and ensuring that data flows are efficient, scalable, and aligned with business objectives. Your expertise will help clients manage, secure, and optimize their data assets, enabling them to make informed, data-driven decisions in fields such as research, finance, healthcare, and more.

Key Responsibilities:

• Lead the design and architecture of data management systems, ensuring that data is collected, stored, and managed efficiently across multiple platforms, including HPC, cloud, and hybrid environments.
• Develop and implement data governance frameworks that ensure data integrity, security, and compliance with regulatory requirements (e.g., GDPR, HIPAA).
• Collaborate with stakeholders to define data strategies, aligning data architectures with business goals, performance requirements, and scalability needs.
• Design and optimize data pipelines for large-scale data processing, ensuring high availability, low latency, and efficient use of resources in HPC environments.
• Architect data integration solutions, enabling seamless data flow between multiple sources, including structured, semi-structured, and unstructured data types.
• Oversee the implementation of data security best practices, ensuring data protection at rest and in transit, and implementing encryption, access controls, and secure data sharing policies.
• Work closely with data scientists, analysts, and engineers to design data models that support AI/ML workloads, big data analytics, and large-scale simulations.
• Collaborate with DevOps and infrastructure teams to implement data storage solutions that are optimized for performance, cost-efficiency, and scalability.
• Conduct data quality assessments, developing processes for data validation, cleansing, and transformation to ensure high-quality data is used for analysis and decision-making.
• Lead efforts in metadata management and the creation of data catalogs to improve discoverability, traceability, and documentation of data assets.
• Guide clients on the adoption of modern data architectures, including data lakes, data warehouses, and cloud-based storage, ensuring that these solutions are optimized for their specific workloads and operational needs.
• Stay up-to-date with emerging trends and technologies in data architecture, AI/ML, and big data analytics, continuously improving data strategies to support evolving client needs.

Requirements:

• 8+ years of experience in data architecture, data management, or information systems, with a focus on large-scale or HPC environments.
• Proven experience in data modeling, database design, and data integration strategies for complex, large-scale systems.
• Expertise in data governance and regulatory compliance (e.g., GDPR, HIPAA), with a focus on data protection, privacy, and security best practices.
• Strong knowledge of data warehousing, data lakes, and modern data storage architectures, including cloud-based solutions like AWS, Azure, and Google Cloud.
• Experience with ETL/ELT processes, including tools such as Apache NiFi, Airflow, or similar data orchestration frameworks.
• Proficiency in designing and managing data pipelines for processing large volumes of data across distributed systems, HPC environments, or big data platforms (e.g., Hadoop, Spark).
• Familiarity with AI/ML data requirements, including data labeling, feature engineering, and model-ready data structures.
• Strong understanding of metadata management, data lineage, and data cataloging tools to ensure transparency and traceability of data assets.
• Experience in data security, encryption, and access control mechanisms to ensure that data assets are securely managed across environments.
• Strong communication skills with the ability to work with technical and non-technical stakeholders to define data strategies, present data architectures, and drive consensus on best practices.

Preferred Qualifications:

• Experience with cloud-based data management solutions such as Amazon Redshift, Google BigQuery, or Azure Data Lake.
• Certifications in data architecture or related fields (e.g., CDMP – Certified Data Management Professional).
• Familiarity with DevOps for data environments, including CI/CD pipelines for data infrastructure and automation of data management processes.
• Knowledge of data virtualization and real-time data streaming technologies (e.g., Apache Kafka, Flink).
• Experience in quantum computing data architectures or cutting-edge technologies related to future data processing needs in HPC environments.