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[C3I] Data Architect - Governance & Quality at Software Mind · globalremotely

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 cooperation with enjoyment.

Job Description

Project – the aim you’ll have

We are the Data & Analytics Global Capability at Software Mind. We design, build and maintain data and analytics solutions for clients across industries: financial services, telco, media & entertainment, retail, manufacturing, gaming, healthcare and more. Our projects run on modern data platforms, mainly Snowflake and Databricks on Azure, AWS and GCP, as well as on-premises and hybrid environments.

We are looking for a Data Architect / Data Governance expert 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.

Role – 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.
Presales & Client Advisory:
Partner with sales and industry teams as technical presales: discovery workshops, solution design, estimations and the technical parts of proposals and RFPs.
Present architectures and recommendations to client stakeholders, from engineering teams to C-level.
Design and run PoCs and technical demos.
Evaluate new approaches and tools (data mesh, streaming-first, AI-native platforms) and share what you learn internally and externally.

Qualifications

Expectations – the experience you need

Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects.
Experience in data modeling (conceptual, logical, physical), preferably in a large organization or within complex data ecosystems.
Deep knowledge of modern Data Platforms (Snowflake/Databricks)
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, GCP, 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 (reading documentation, community discussions).

Additional skills – the edge you have

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.