Information Architect
1 month ago
Must-Have skills:
Data Architecture: A strong background in data architecture, including experience in architecting large-scale data solutions, data migration, data warehousing, and familiarity with cloud-based data platforms (e.g., Snowflake).
Data Strategy and Governance: Experience in defining and operationalizing data strategy, data governance, data lineage, and quality standards. The ability to contribute to the development of a data life cycle governance framework.
Data Modeling: Proficiency in data modeling, including conceptual and logical data modeling. Ability to create and deliver Conceptual and logical Data Models.
Data Engineering: Extensive knowledge of data engineering, data integration, and data management concepts, such as APIs, ETL (Extract, Transform, Load), MDM (Master Data Management), CRUD (Create, Read, Update, Delete), Pub-Sub (Publish-Subscribe).
Structured and Hierarchical Datasets: Experience with both structured and hierarchical datasets, such as JSON and XML.
System Integration and Analytics: Engineering experience with large-scale system integration and analytics projects, demonstrating the ability to work on high-level strategy and low-level tactical integration.
Essential Job Functions and Responsibilities:
Provide partnership, domain expertise, direction, and accountability for strategic architecture plans, system design, and implementation standards for the data domain.
Lead efforts to ensure data capabilities are operating efficiently, accurately, and with high quality.
Collaborate with programs & projects to understand data dependencies, anticipate risks, and identify opportunities. Guide data project decisions based on the direction of data domain roadmap.
Contribute data domain expertise to feasibility, complexity assessments of potential future initiatives including data domain analysis.
Develops an integrated view of data capabilities and processes, using a repeatable approach, cohesive framework, and available industry best practices and techniques.
Provides consulting and makes recommendations to tackle data transformation initiatives, help mature existing data capabilities, or enable new data capabilities.
Facilitate the process of defining and publishing data standards, best practices, data capabilities, and data roadmap, synchronized with the overall enterprise future state architecture.
Collaborate in creation of conceptual and logical architecture and models following the enterprise guidance to describe a particular domain of data and use these models to inform the physical design of data-related projects.
Co-develop data life cycle governance framework including standards, patterns, and controls.
Deliverables:
Domain Analysis Specification & Findings:
Clearly define the data domain and its confines.
Leverage an organizationally accepted and standardized framework (like CMMI) to evaluate and grade maturity within the defined data domain.
Identify opportunities for investment along with specified benefits for investment, including the data capability roadmap.
Document Data Governance Capabilities (co-create)
Data and attribute definitions and master dataset definitions within the data domain.
Documentation of domain definition governance processes and platform.
Documented capabilities and coverage for data lineage within the data domain.
Diagrams overall data landscape within a data domain, including current and future state data flows.
Data mapping document (initiate support).
Assist in the creation and delivery of Conceptual & Logical Data Model by providing business level understanding of relationships to other domains and business transactions across the enterprise.
Soft Skills:
Consulting mindset – highly collaborative, highly communicative approach with an eye on influence, rather than control.
Ability to work on high-level strategy and low-level tactical integration along with stakeholders at all levels of the organization.
Ability to communicate complex systems and concepts through pictures.
Clear and concise communication skills – both written and oral.
Remains unbiased to specific technology or vendor – more interested in results.
Qualifications:
Minimum eight years of relevant experience as a data architect or data engineer building large-scale data solutions.
Bachelor's degree in Engineering, Information Technology, Computer Science, or a related field.
Experience in architecting and large data modernization, data migration, data warehousing – experience with cloud-based data platforms (like Snowflake).
Experience with defining and operationalizing data strategy, data governance, data lineage and quality standards.
Extensive knowledge of data engineering, data integration and data management concepts (i.e. APIs, ETL, MDM, CRUD, Pub Sub, etc.).
Experience with data modeling.
Experience with structured and hierarchical datasets (i.e. JSON, XML, etc.).
Engineering experience with large scale system integration and analytics projects.
P&C domain experience a plus.
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