Design and standardize enterprise semantic, logical, and conceptual data models; define corporate data model and reference architectures; integrate models with data catalog and metadata tools; support implementation, data quality, lineage, and governance; document metamodels and resolve data issues across domains.
Company Description
Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment.
Job DescriptionProject – the aim you’ll have
We are a team that develops and maintains analytical solutions for digital services (VOD, news, business, lifestyle, entertainment). We are building a modern data ecosystem based on a broad range of technologies and the AWS cloud.
We are looking for a Corporate Data Architect who will help organize and standardize how the organization understands and uses data. Your main task will be to design and develop data architecture at the level of the entire organization so that data is consistent, well defined, and ready to be used in analytics, AI, and system integrations. You will act as a bridge between the worlds of business, data, and technology, connecting strategy, standards, and practice.
Position – how you’ll contribute
- 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 reference architecture (logical and technical reference architectures) and design patterns for data domains.
- Co-create the strategy and roadmap for data architecture development that supports the vision of the Data Governance program and organizational goals.
- Collaborate with Data Governance, Data Management, Analytics, AI, and Data Engineering teams to integrate models with the data catalog and metadata management tools.
- Support technical teams in implementing models in data layers.
- Participate in designing data integration, flow, and quality processes (data lineage, data contracts, data quality).
- Identify and certify authoritative data sources within organizational domains.
- Monitor the compliance of data solutions with Data Governance policies and standards as well as architectural recommendations.
- Support the resolution of data conflicts and issues.
- Create architectural documentation, metamodels, and inter-domain relationship diagrams.
- Consult on and develop good practices in the area of modeling, metadata, and information architecture.
- Collaborate on defining the vision, priorities, and directions for the development of data architecture in the organization.
Expectations – the experience you need
- Experience in data modeling (conceptual, logical, physical), preferably in a large organization or within complex data ecosystems.
- Practical knowledge of tools and methodologies such as ER, UML, IDEF1X, Data Vault, 3NF, Kimball/Inmon.
- Ability to work with data catalogs and metadata management tools (e.g. DataHub, Collibra, Alation, Atlan).
- Knowledge of SQL and relational data models.
- Experience working with database systems and data architecture in a cloud environment (AWS).
- 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 (reading documentation, community discussions).
Additional skills – the edge you have
- Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects.
- Knowledge of concepts and technologies related to the semantic data layer: ontologies, RDF, OWL, GraphQL, dbt Semantic Layer, Semantic Kernel.
- Knowledge of Python for automation, model validation, and API integrations.
- Knowledge of event-driven data architecture.
- Experience in creating and maintaining architectural documentation.
Our offer – professional development, personal growth
- Flexible employment and remote work
- International projects with leading global clients
- International business trips
- Non-corporate atmosphere
- Language classes
- Internal & external training
- Private healthcare and insurance
- Multisport card
- Well-being initiatives
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