Enterprise Data Architect

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Job description

Whether you're an investment professional, an expert in sales or a technology specialist, you'll find a culture at MFS that supports you in doing what you do best. Our employees work together to reach better outcomes, always favoring the strongest idea over the strongest individual. We put people first and show care and compassion for our community and each other. Because what we do matters - to us as valued professionals and to the millions of people and institutions who rely on us to help them build more secure and prosperous futures.

Job Description For a leading and rapidly growing Asset Management firm and in conjunction with the Investment Data Office, the Investment Data Architect contributes to a long-term strategic initiative to unify and harmonize our investment data. This initiative to harmonize investment data enables enhanced investment decision making, risk management and client reporting for our multi-asset platform by delivering consistent, timely, accurate and user-friendly data to investors, risk teams and clients.

The Investment Data Architect will analyze, assess, document and design scalable and sustainable data architecture and data management solutions, addressing critical investment data goals and desired outcomes. The Investment Data Architect will partner with cross-functional teams to provide subject matter expertise on data architecture that includes designing, creating, deploying, managing, and sustaining solutions that align with the Investment Data Management strategy. The Investment Data Architect will oversee and must have intimate knowledge of investment data domains including but not limited to securities (e.g., Equities, Fixed Income, Derivatives, Structured Products), analytics (e.g., last price, yield to maturity, average life, duration), cash, portfolios, transactions, legal entities, indexes, market data (e.g., pricing, ratings), and reference data (e.g., calendar, country currency).

Reporting to the Director of Investment Data Harmonization, and working in conjunction with the Enterprise Data Architecture team, this position will ensure that all enterprise standards, guidelines, practices, and metrics are defined and followed within the Investment Data implementations.

PRINCIPAL RESPONSIBILITIES :

• Drives the investment data architecture and technology vision to implement solutions supporting the Investment Data Management strategy and desired business outcomes.
• Understands the needs for investment data; and supports the exploration and thinking for how the investment data needs may be satisfied by information and technology.
• Leads the overall design of the investment data architecture and data models, ensuring investment data is available and shared consistently; provides and uses expert knowledge in design activities to translate high-level business requirements into data model and appropriate metadata, test data, and data quality standards
• Creates and maintains blueprints for current and target-state reference architectures for investment data and their inclusion in enterprise and line of business reference architectures.
• Defines patterns and standards for investment data platforms, design, engineering, and development; and continuously drives out ambiguity and streamline data architectures.
• Develops and sustains conceptual, logical, and physical data models to support the investment data management strategy and ensures alignment with the enterprise data strategy.
• Promotes alignment of the Investment Data Management strategy with the enterprise data strategy and standards.
• Contributes to the understanding and evolution of a modern data architecture to support best in class data management.
• Works in a complex data environment in an engineering, design and architecture capacity; including enterprise warehouses, marts, ODS', and cloud data platform technologies.
• Supports and provides expertise to Data Trustees, Investment Data Product Owner(s), the Investment Data Leader, and the governance bodies with the development, sustainment and usage of the conceptual data model, taxonomies, and business glossary for investment data.
• Works with Investment Data Office team members, business analysts, Investment and Operations team members, and IT teams to understand investment data consumers' needs and develop solutions.
• Proactively investigates, proves out and incorporates new technologies and capabilities as required.
• Drives automation, data quality, security and governance practices for investment data.
• Ensures investment data management architecture patterns and practices comply with regulatory requirements.

JOB REQUIREMENTS :

• Bachelor's Degree with a major in Computer Science, Business Data Processing, Computer Information Systems or related field is required.
• Minimum seven (7) years' data modeling experience in an agile work-environment delivering transactional and operational, reporting/analytical (EDW/Data Lake/NoSQL) solutions.
• Minimum five - eight (5-8) years relational database (Sybase, Oracle or MS SQL server) experience (SQL scripts, stored procedures, triggers, etc.).
• 3 years' experience within the investment and asset management industry, high level knowledge of Charles River, Eagle, IBOR technologies and master data management solutions.
• Understanding of metamodels, taxonomies, ontologies, and challenges of applying structured techniques (data modeling) to less-structured sources.
• Experience with data modeling tools (Embarcadero ER/Studio preferred), experience with diverse modeling methods or techniques (e.g., entity relationship, hierarchical, relational, network/graph, object-oriented, knowledge graphs, UML, etc.), and expertise in data modeling within an agile software development paradigm.
• Experience with ETL (Informatica's PowerCenter), data quality, data profiling and ad-hoc query tools.
• Extensive hands-on experience in data warehousing design, ETL/ELT, BI/Analytics Data Repositories development.
• Understanding of enterprise and reporting modeling concepts, including dimensional modeling, snowflakes, slowly changing dimensions, schema on read/write, irregular dimensions, and surrogate, compound and intelligent keys.

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