[C3I] Data Architect - Governance & Quality

Software Mind

23d

About The Position

Software Mind’s Data & Analytics Global Capability designs, builds, and maintains data and analytics solutions for clients across industries. Projects use modern data platforms, mainly Snowflake and Databricks on Azure, AWS, and GCP, as well as on-premises and hybrid environments. The Data Architect / Data Governance expert will help organize and standardize how an organization understands and uses data. The role designs and develops organization-wide data architecture so data is consistent, well defined, and ready for analytics, AI, and system integrations. It bridges business, data, and technology by connecting strategy, standards, and practice. The position also contributes to presales and client advisory work, including discovery workshops, solution design, estimates, proposals and RFPs, client presentations, proofs of concept, and technical demos. The architect evaluates emerging approaches and tools and shares findings internally and externally.

Requirements

Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects.
Experience in conceptual, logical, and physical data modeling, preferably in a large organization or complex data ecosystem.
Deep knowledge of modern data platforms such as Snowflake and Databricks.
Practical knowledge of data modeling tools and methodologies such as ER, UML, IDEF1X, Data Vault, 3NF, Kimball, and Inmon.
Ability to work with data catalogs and metadata management tools, such as DataHub, Collibra, Alation, or Atlan.
Knowledge of SQL and relational data models.
Experience working with database systems and data architecture in a cloud environment, such as AWS, GCP, or Azure.
Ability to work with engineering and analytics teams to translate business needs into data models.
Understanding of data security, availability, and control principles.
Good command of English for reading documentation and community discussions.

Nice To Haves

Knowledge of semantic data layer concepts and technologies, including ontologies, RDF, OWL, GraphQL, dbt Semantic Layer, or Semantic Kernel.
Knowledge of Python for automation, model validation, and API integrations.
Knowledge of event-driven data architecture.
Experience creating and maintaining architectural documentation.

Responsibilities

Design semantic, logical, and conceptual data models based on business domains and organizational processes.
Define and standardize the corporate data model as part of the Data Governance initiative.
Create and maintain logical and technical reference architectures and design patterns for data domains.
Co-create the data architecture strategy and roadmap in support of the Data Governance program and organizational goals.
Collaborate with Data Governance, Data Management, Analytics, AI, and Data Engineering teams to integrate models with data catalogs and metadata management tools.
Support technical teams in implementing models in data layers.
Participate in designing data integration, data flow, and quality processes, including data lineage, data contracts, and data quality.
Identify and certify authoritative data sources within organizational domains.
Monitor data solutions for compliance with Data Governance policies, standards, and architectural recommendations.
Support resolution of data conflicts and issues.
Create architectural documentation, metamodels, and inter-domain relationship diagrams.
Consult on and develop good practices for modeling, metadata, and information architecture.
Collaborate on defining the vision, priorities, and directions for organizational data architecture.
Partner with sales and industry teams on presales activities, including discovery workshops, solution design, estimations, and technical sections of proposals and RFPs.
Present architectures and recommendations to client stakeholders, from engineering teams to C-level.
Design and run proofs of concept and technical demos.
Evaluate new approaches and tools, including data mesh, streaming-first, and AI-native platforms, and share findings internally and externally.