Lead Data Architect

The Data Architect will be responsible for designing, creating, deploying, and managing the organization's data architecture. This role involves defining how the data will be stored, consumed, integrated, and managed by different data entities and IT systems, as well as any applications using or processing that data.

Local Candidates Only.

Must be able to commute to Foxboro, MA as needed for meetings.

Key Stakeholder Relationships:

• vCISO/CISO: Ensure Data Security standards, including PII governance is followed
• Enterprise Architect: In support of a vibrant Architecture Community and ensuring coordinated recommendations and guidelines for the CP technical community.
• Product Owners: Set guidelines and standards re: Data oriented requirements
• Data Engineers: Collaborative partnership to design and build required data infrastructure
• Scrum Teams: Consultative resource to support guidelines, standards and provide implementation guidance
• Low-Code/No-Code Team(s) and Business Leaders: Set guidelines and standards regarding the entry and maintenance of Data

Responsibilities:

• Design and implement effective database solutions and models to store and retrieve company data.
• Analyze structural requirements for new software and applications.
• Migrate data from legacy systems to new solutions.
• Design conceptual and logical data models and flowcharts.
• Improve system performance by conducting tests, troubleshooting, and integrating new elements.
• Optimize new and current database systems.
• Define security and backup procedures.
• Coordinate with the Data Science department to identify future needs and requirements.
• Provide operational support for Management Information Systems (MIS).
• Ensure compliance with data governance and security policies.
• Knowledge of data governance and data quality management, including approaches to identify and interpret data lineage and provenance.
• Familiarity and use of Cloud based data management and visualization platforms, Azure preferred

Qualifications:

Educational Background:

Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.

Technical Skills:

Proficiency in data modeling and database design.

Strong knowledge of SQL and experience with database management systems (DBMS) such as Oracle, SQL Server, or MySQL.

Experience with data warehousing solutions and ETL (Extract, Transform, Load) processes.

Familiarity with big data technologies like Hadoop, Spark, or similar.

Understanding of cloud platforms such as Azure (preferred), AWS or Google Cloud.

Knowledge of data governance and data security practices.

Expertise in machine learning algorithms and frameworks.

Experience with large language models (LLMs) and natural language processing (NLP) techniques.

Familiarity with transformer architectures and their applications in NLP.

Experience:

Minimum of 8 years of experience in data architecture, data management, or a related field.

Proven experience in designing and implementing data solutions for large-scale projects.

Experience with data integration and data migration projects, including data shared between proprietary software and low-code/no-code systems

Hands-on experience with machine learning model development and deployment.

Experience in fine-tuning and optimizing LLMs for specific business needs.

Soft Skills:

Strong analytical and problem-solving skills.

Excellent communication and collaboration skills to work with cross-functional teams.

Ability to translate business requirements into technical specifications.

Certifications (Preferred):

Relevant certifications such as Certified Data Management Professional (CDMP), Azure Data Fundamentals/Data Scientist Associate, AWS Certified Big Data – Specialty, or similar.

Certifications in machine learning or AI, such as TensorFlow Developer Certificate, Azure AI Engineer Associate or AWS Certified Machine Learning – Specialty.

Additional Requirements:

Ability to work in a fast-paced environment and manage multiple priorities.

Strong attention to detail and commitment to data quality.

Knowledge of the latest advancements in AI and machine learning, including multimodal AI and generative models.