Data Architect – Azure Data Platform & AI

Cognizant; Brisbane; On-site; AUD 100,000 - 150,000; Full time

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.

Skills

Microsoft Fabric

Azure Databricks

Databricks SQL

PySpark

ETL/ELT

Data Modeling

Data Warehousing

Power BI

Generative AI

Python

Azure DevOps

CI/CD

Tools

Azure Data Factory

Azure Synapse

Delta Lake

Unity Catalog

OneLake

Job description

What makes Cognizant a unique place to work? The combination of rapid growth and an international and innovative environment! This is creating many opportunities for people like YOU — people with an entrepreneurial spirit who want to make a difference in this world.

At Cognizant, together with your colleagues from all around the world, you will collaborate on creating solutions for the world’s leading companies and help them become more flexible, more innovative, and successful. Moreover, this is your chance to be part of the success story.

Position Summary:

We are seeking an experienced Data Architect with deep expertise in Azure Data Engineering, Microsoft Fabric, Databricks, PySpark, ETL/ELT development, Data Modeling, Data Warehousing, AI/Generative AI, and cloud-based analytics platforms. The ideal candidate will be responsible for defining and implementing enterprise data architecture, designing scalable and reusable data models, and delivering modern data solutions that support analytics, reporting, machine learning, and AI-driven initiatives.

The role requires hands-on experience in designing, building, optimizing, and supporting scalable data solutions across batch, streaming, API-based, and file-based ingestion frameworks. The candidate should possess strong knowledge of conceptual, logical, and physical data modeling, dimensional modeling, star and snowflake schemas, data governance, master data management, and enterprise data integration patterns.

Experience with Microsoft Fabric, including OneLake, Lakehouse, Data Factory, Data Warehouse, Real-Time Analytics, and Power BI, is required. Additionally, hands-on experience with AI and Generative AI solutions, including Azure AI Services, Azure OpenAI, Retrieval-Augmented Generation (RAG), vector databases, AI-powered analytics, Copilot implementations, and machine learning integration with data platforms, is highly desirable.

The successful candidate will be responsible for delivering reliable, secure, scalable, and high-performing data pipelines while collaborating with business stakeholders, solution architects, and cross-functional delivery teams in Agile and Waterfall environments. The role also includes driving data strategy, ensuring data quality, performance optimization, security, governance compliance, and enabling advanced analytics and AI capabilities across modern cloud-based data ecosystems.

Mandatory Skills:

• 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, Azure AI Services, 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.

Roles And 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.

Qualifications/Certifications:

• 16+ years of IT experience in data engineering, data warehousing, ETL/ELT development, cloud data platforms, and technical delivery leadership.
• Microsoft Certified: DP-900 Microsoft Azure Data Fundamentals.
• Microsoft Certified: AZ-900 Azure Fundamentals.
• Databricks Data Engineer Associate Certification.
• Led modern data platform development using ADF, Databricks, Synapse, Cosmos DB, Kafka.
• Built scalable ingestion and transformation pipelines.
• Generative AI (ChatGPT, Copilot) for data insights and automation.
• Prompt Engineering & AI‑assisted analytics.
• API Integration & AI model consumption.
• Machine Learning fundamentals.

Salary Range: >$100,000
Date of Posting: 27-Oct-26

Cognizant is committed to providing Equal Employment Opportunities. Successful candidates will be required to undergo a background check