Senior Data Architect: Azure Data Platform & AI

Cognizant

Brisbane

On-site

AUD 100,000 - 150,000

Full time

2 days ago

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Job summary

Cognizant is seeking an experienced Data Architect in Brisbane to lead enterprise data platforms, focusing on Microsoft Fabric, Azure Databricks, and AI-powered analytics. You will design scalable data models, govern data, and deliver modern data solutions supporting analytics, ML, and AI initiatives.

The role requires hands-on experience with batch, streaming, and API data ingestion, data governance, and enterprise integration patterns.

Qualifications

Proven hands-on experience in designing, architecting, developing, and delivering enterprise-scale data engineering, analytics, and AI-powered solutions using Microsoft Fabric, Azure Databricks, Databricks SQL, Databricks Workflows, PySpark, and cloud-native technologies across complex business environments.
Strong expertise in designing and implementing scalable data architectures, distributed data pipelines, ETL/ELT frameworks, real-time and batch processing solutions, and high-performance analytics platforms leveraging modern cloud ecosystems.
Extensive experience with Microsoft Fabric (OneLake, Lakehouse, Data Factory, Data Warehouse, Real-Time Analytics, and Power BI) and Azure Databricks, including Unity Catalog, Delta Live Tables (DLT), Delta Lake, and Azure Data Lake Storage (ADLS) for secure, governed, and reliable data management.
Demonstrated capability in architecting and implementing enterprise-scale Lakehouse and Data Mesh solutions, ensuring data quality, governance, lineage, security, compliance, and operational excellence.
Strong expertise in data modeling, including conceptual, logical, and physical data models, dimensional modeling, star and snowflake schemas, data vault modeling, and semantic layer design to support analytics and business intelligence requirements.
Proficiency in Python, PySpark, SQL, and Spark-based frameworks for developing robust, optimized, reusable, and scalable data processing and analytical workloads.
Experience in designing and implementing AI/Generative AI solutions, including Azure OpenAI, Retrieval-Augmented Generation (RAG), vector databases, AI-powered analytics, and enterprise Copilot integrations.
Strong experience with Azure DevOps, CI/CD pipelines, Infrastructure as Code (IaC), release management, version control, and Agile delivery methodologies to support continuous integration and deployment practices.
Deep understanding of data architecture principles, cloud architecture patterns, performance optimization techniques, security frameworks, governance models, and cloud-native solution design for large-scale enterprise data ecosystems.
Experience in requirements analysis, solution architecture, effort estimation, resource planning, proposal preparation, technical leadership, stakeholder management, and delivery governance for data modernization and digital transformation initiatives.
Strong exposure to Agile ways of working, including Sprint Planning, Backlog Grooming, PI Planning, SAFe practices, Retrospectives, and cross-functional collaboration across distributed teams.
Excellent communication, presentation, consulting, and leadership skills with the ability to influence strategic decisions, establish trusted client relationships, define data strategy and roadmaps, and drive customer‑centric business outcomes.
Experience in defining and implementing data governance, metadata management, master data management (MDM), data quality frameworks, and enterprise data standards to support regulatory and business requirements.

Responsibilities

Collaborate with business stakeholders, solution architects, AI specialists, and development teams to gather, analyze, and translate complex business requirements into scalable data, analytics, and AI‑driven platform solutions.
Lead end‑to‑end delivery of enterprise Data Warehouse, Data Lake, Lakehouse, Analytics, and AI initiatives using Microsoft Fabric, Azure Data Factory, Azure Databricks, Azure Synapse, Azure Data Lake Storage Gen2, and Azure AI Services.
Design and implement robust ETL/ELT frameworks for batch, streaming, API‑based, and file‑based data ingestion using Microsoft Fabric Data Factory, Python, PySpark, Event Hub, Databricks, and cloud‑native integration services.
Perform source system analysis, data profiling, data discovery, and data mapping activities to ensure accurate, secure, and efficient migration and integration of business‑critical data assets.
Develop and optimize data pipelines, transformation frameworks, semantic models, and analytical solutions to improve performance, scalability, and operational efficiency across enterprise data platforms.
Establish and enforce data quality, validation, governance, metadata management, and lineage standards by implementing automated monitoring, reconciliation, and compliance controls across data ecosystems.
Lead the design and implementation of Lakehouse architectures, enterprise data models, and AI‑ready data foundations to support advanced analytics, machine learning, and Generative AI use cases.
Design, develop, and integrate AI/Generative AI solutions, including Azure OpenAI, Retrieval-Augmented Generation (RAG), vector search, AI‑powered analytics, and enterprise Copilot capabilities, leveraging governed enterprise data assets.
Lead technical teams across multiple projects, providing guidance on architecture, data modeling, AI integration, design patterns, coding standards, performance tuning, security, and cloud best practices.
Manage the complete project delivery lifecycle, including requirements gathering, solution architecture, estimation, sprint planning, development, testing, deployment, production support, and continuous improvement initiatives.
Drive Agile delivery practices through sprint planning, backlog refinement, status reporting, dependency management, risk mitigation, and stakeholder communication using Azure DevOps, Jira, and Microsoft Fabric monitoring capabilities.
Work closely with business users, data stewards, data owners, and cross‑functional teams to resolve data‑related issues and ensure successful implementation of business and AI requirements.
Design and implement conceptual, logical, and physical data models, including dimensional models, Fact and Dimension tables, Data Vault structures, and semantic layers, leveraging industry‑standard methodologies such as SCD Type 1 and Type 2.
Architect and implement Microsoft Fabric solutions, including OneLake, Lakehouse, Data Warehouse, Data Factory, Real‑Time Analytics, and Power BI, to deliver unified and scalable enterprise analytics platforms.
Facilitate customer engagements, AI strategy workshops, technical design sessions, solution walkthroughs, and architecture reviews to align business objectives, data strategy, and AI transformation roadmaps with technical deliverables.
Define and promote enterprise data governance, security, privacy, and compliance standards while enabling self‑service analytics, trusted data products, and AI innovation across the organization.