Data Architect

Brak informacji o wynagrodzeniu

NieokreśloneOn-siteFull-time

#466420·Dodano 19 dni temu·10

Źródło: LinkGroup

Tech Stack / Keywords

Azure DatabricksLakehouseETL/ELT Microsoft AzureAzure Data Lake StorageMicrosoft FabricApache AirflowUnity CatalogTerraformAzure DevOps

Podsumowanie oferty

Shape and evolve an enterprise data platform for reliable data integration, advanced analytics, and data-driven decisions. Define its end-to-end architecture and standards, working with engineering teams and business stakeholders; the platform is based on Lakehouse architecture and Azure Databricks.

Wymagania

Required Experience and Skills:

Proven experience designing and developing scalable enterprise data platforms based on Lakehouse architecture.
Strong knowledge of Azure Databricks and modern cloud-based data processing environments.
Experience creating conceptual, logical, and physical data models.
Practical knowledge of data integration, including ETL/ELT, batch processing, streaming, and data exchange between operational and analytical systems.
Experience defining architectural standards, reusable patterns, and best practices for data modelling, integration, security, and data access.
Strong understanding of data architecture principles and the relationship between data platforms, reporting, and analytics.

Data Modelling and Architecture Documentation:

Advanced knowledge of data modelling techniques, including ERD and UML.
Experience using Enterprise Architect or comparable tools to document data models, architectures, and data flows.
Ability to manage and maintain an architecture repository containing data models, standards, architectural decisions, integration patterns, and reference documentation.

Nice to have:

Familiarity with data management and enterprise architecture frameworks such as DAMA-DMBOK and TOGAF.

Obowiązki

Key Responsibilities:

Design and evolve end-to-end data architecture, including data models, ingestion patterns, transformation layers, data products, and consumption interfaces.
Define architectural principles and technical standards for data storage, processing, integration, access, and sharing.
Design scalable data integration solutions for batch and streaming workloads, ETL/ELT processes, and connections between source systems and analytical environments.
Establish best practices for data modelling, metadata management, data lineage, reference data, and business terminology.
Maintain architectural documentation, including data models, integration patterns, design decisions, standards, and reference materials.
Translate business and technical requirements into architectural designs, data structures, and implementation recommendations.
Conduct architecture reviews, document key decisions, and guide delivery teams in applying agreed standards and security principles.
Work with Data Engineers, Analysts, Data Owners, Project Managers, and business stakeholders to ensure solutions meet organizational requirements.
Present architectural recommendations and platform development options to technical and business audiences.

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