Principal Data Architect

Curium Curium•Westport, CT

2d•Hybrid

About The Position

The Principal Data Architect designs, develops, and governs Curium’s enterprise data architecture in support of business intelligence, analytics, artificial intelligence, operational reporting, and digital transformation. The role establishes data standards, models, integration patterns, and governance frameworks to make enterprise data trusted, scalable, and secure. The architect works with business stakeholders, data engineers, analytics teams, application owners, and technology leaders to align data assets with organizational strategy and business objectives. The position develops enterprise data strategies, roadmaps, standards, reference architectures, and documentation; designs data platforms and conceptual, logical, physical, and dimensional models; and defines patterns for data integration, governance, quality, security, and compliance. It also enables governed reporting, self-service analytics, AI, and reusable data products, while evaluating emerging technologies and guiding delivery teams through architecture practices and code reviews. The work schedule is Monday through Friday, 8 a.m. to 5 p.m. The position is hybrid, with four days onsite and one day remote.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field, or equivalent professional experience.
At least 8 years of professional software engineering experience, including at least 4 years in data management, data engineering, data warehousing, or analytics, and at least 2 years of lead-level cloud data engineering.
Demonstrated experience designing and implementing enterprise-scale data solutions for business intelligence, analytics, operational reporting, and advanced analytics.
Strong experience developing conceptual, logical, and physical data models, including dimensional star and snowflake schemas and modern data warehouse, data lake, and lakehouse architectures.
Knowledge of established data modeling principles and best practices.
Strong understanding of data integration and engineering principles, including ETL/ELT processes, data pipelines, APIs, batch processing, and real-time integration patterns.
Proficiency in SQL and strong understanding of database design, data transformation, performance optimization, and data management principles.
Experience with data governance practices, including data ownership, stewardship, metadata management, data lineage, data quality, master and reference data, and data cataloging.
Strong understanding of data security, privacy, access management, and regulatory compliance requirements, and the ability to incorporate these into enterprise data architecture.
Strong communication and stakeholder management skills, including the ability to explain complex technical concepts in clear business language, establish architecture standards, influence technical decisions, and provide technical leadership across teams without direct authority.
Strong analytical and problem-solving skills, with the ability to translate business requirements into scalable, secure, and maintainable data architecture solutions.

Nice To Haves

Experience designing solutions on modern cloud-based data platforms and analytics or distributed-data environments such as Microsoft Fabric, Azure, Snowflake, Databricks, or comparable technologies.
Master’s degree or MBA.
Knowledge of Agile development and the software development lifecycle.
Relevant certifications in data management, analytics, cloud platforms, architecture, or project management.
Experience with pharmaceutical regulations and guidance, including 21 CFR Part 11/210/211, EU Annex 11, MHRA, GAMP 5, cGMP, electronic records, signatures, and data integrity.
Additional language proficiency.

Responsibilities

Develop and maintain enterprise data strategies, roadmaps, standards, reference architectures, and documentation.
Design scalable data platforms and conceptual, logical, physical, and dimensional models for reporting, analytics, AI, and operational use.
Define enterprise principles for master, reference, transactional, and cross-domain data.
Define scalable standards and patterns for ETL, ELT, APIs, batch and real-time integration, automated onboarding, and ingestion management.
Design integration across applications, platforms, and data sources in partnership with Data Engineers.
Establish governance and quality standards for metadata, lineage, ownership, stewardship, access, and consistent business definitions.
Enable trusted data products through metadata-driven auditing and monitoring, collaborating with Data Governance teams.
Design secure and resilient cloud data warehouses, lakes, and lakehouses to meet scalability, availability, performance, and cost requirements.
Evaluate emerging technologies, third-party solutions, and AI tooling; recommend improvements and establish usage and cost-attribution practices.
Enable governed reporting, self-service analytics, AI, and reusable data products through trusted data and well-defined semantic models.
Partner with analytics and AI teams to define architecture requirements and secure adoption standards.
Embed security, privacy, role-based access, and regulatory requirements into architecture designs and data protection standards.
Partner with Security and Compliance teams to align with organizational and regulatory expectations.
Maintain operational compliance with applicable US and international regulatory agencies and guidelines, including FDA, DEA, EU, HC, TGA, PIC/S, ISO, USP, NRC, and cGMP.
Provide technical leadership, translate business needs into scalable solutions, and review designs for alignment with standards.
Collaborate across business, technology, and governance teams, maintaining diagrams, standards, and architecture documentation.
Guide and mentor Data Engineers and delivery teams in architecture best practices and code reviews.