Senior Data Architect

Manulife•Boston, MA

9d•CA$113,000 - CA$163,000•Hybrid

About The Position

The Senior Data Architect is responsible for defining and leading the segment data architecture strategy that enables trusted, scalable, secure, and AI-ready data capabilities across the organization. This role provides architectural leadership for architecture, design, governance, modernization, and implementation of data solutions and platforms that support analytics, reporting, machine learning, and AI-driven solutions, while remaining actively engaged in hands-on solution design, data platform engineering, and implementation activities where deep technical expertise is required. Partner with business and technology stakeholders, architects and engineers to define data requirements, data domains, data consumption patterns, and architect scalable and secure data solutions that support business outcomes.

Requirements

10+ years of experience architecting, designing, and delivering large-scale data solutions.
Hands-on experience with one or more modern data platforms, such as Microsoft Fabric, Azure Data Factory, Azure Synapse Analytics, Databricks, Spark, or comparable technologies.
Working knowledge of SQL and NoSQL databases and proficiency in SQL plus at least one relevant programming or scripting language, such as Python, PySpark, Java, or Scala.
Strong knowledge of enterprise data architecture, data modeling, data integration, and Data Lake, Data Warehouse, and Lakehouse patterns.
Experience designing batch, streaming, and event-driven data solutions for analytical, operational, machine learning, and generative AI workloads.
Strong understanding of Master Data Management (MDM), Reference Data Management, Data Governance, Data Lineage, and Data Quality
Proven experience leading cloud-based data platform implementations and modernization initiatives.
Demonstrated ability to lead architecture strategy while contributing to the implementation of data solutions.
Strong understanding of data security, privacy, regulatory compliance, access control, and responsible data use.
Ability to evaluate data technologies, document architecture decisions, lead architecture reviews, and communicate complex trade-offs to technical and non-technical stakeholders.

Nice To Haves

Experience implementing governed data products, domain-oriented data architectures, or data mesh concepts in a large enterprise environment.
Experience working in a large, complex, highly regulated organization, preferably in financial services, insurance, banking, wealth management, retirement, or another regulated industry.
Familiarity with Machine Learning, MLOps and data architectures supporting generative AI solutions.
Working knowledge of DataOps, CI/CD, automation, performance optimization, monitoring, and operational resilience for data platforms.

Responsibilities

Define and evolve the segment data architecture vision, standards, principles, and roadmap.
Design scalable Data Lake, Data Warehouse, and Lakehouse architectures supporting analytics, operational, and AI workloads.
Establish architecture patterns and standards for data integration, metadata management, data governance, data quality, and data products.
Lead data modernization, migration, and cloud transformation initiatives.
Lead and contribute to the design and implementation of data pipelines, integration frameworks, and cloud-native data platforms.
Provide hands-on technical leadership for complex data engineering initiatives, including data ingestion, transformation, orchestration, and performance optimization.
Design and implement scalable batch, streaming, and near real-time data processing solutions.
Ensure data is secure, governed, and compliant with regulatory and privacy requirements.
Partner with governance, risk, and security teams to promote trusted and responsible use of data.
Design AI-ready data architectures supporting machine learning, advanced analytics, and Generative AI solutions.
Provide technical leadership and architecture governance across strategic data initiatives.
Lead data architecture reviews, technology evaluations, and key design decisions.