Employment type in the employer’s posting: Full timeFrom the employer’s posting
Build graph ingestion pipelines, semantic data products and LLM grounding layers on Azure
Lead design and build of Knowledge Graph solutions within a large program
Design and maintain ontologies, schemas, and reusable engineering patterns
Mentor engineers and review design, code and configuration
Advise clients and support sales, estimation and proposal work
Build graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on Azure
Lead the build of Knowledge Graph solutions that transform client data architecture within a large program scope.
Design, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns
Lead a team or workstream within a larger program, mentor engineers, review design/code/configuration
Develop strong client relationships, earn trust as a key advisor
Required Skills/ Experience Hands-on experience building Azure-based knowledge engineering solutions with Azure Data Lake Storage, Azure Data Factory, Azure Synapse/Fabric, Azure AI Search, Azure OpenAI, Azure Machine Learning, AKS, Microsoft Purview, Entra ID, APIs, and Azure Monitor. Strong Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, and ETL/ELT pipeline tooling. Practical knowledge of NLP and search techniques including entity extraction, entity resolution, semantic search, prompt engineering, LLM grounding, and enterprise-scale LLM applications. Ability to design and implement scalable graph ingestion, ontology/schema pipelines, semantic layers, vector search/retrieval patterns, RAG pipelines, and governed knowledge services. Strong collaboration, technical leadership, delivery ownership, documentation, and stakeholder communication skills. Good to Have Skills 2+ years of hands-on experience with cloud platforms, with Azure specialization and exposure to AWS or GCP in multi-cloud environments. Azure certifications such as Azure Solutions Architect Expert, Azure Data Engineer, Azure AI Engineer, or related credentials. Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, including domain ontologies, data models, compliance needs, and knowledge-driven use cases. External client-facing consulting experience, proposal support, solution shaping, or pre-sales exposure. Advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline broad exposure to diverse ML techniques and agentic systems. 15 years full time education
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale.
In the employer’s words · Read in context
Likely evergreenReposted 3 times since Jul 16, 2026. A role that keeps coming back is often kept open to collect applications rather than to fill one seat.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
Day 57
Similar jobs usually come down between day 8 and day 57.
Well past Accenture’s usual: its jobs come down by about day 1.
From 9,462 similar postings that came down, and 2,606 of Accenture’s. Coming down is not always a hire.
250 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Required Qualifications Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field. Minimum 3 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation. Minimum 3 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation. Minimum 3 years of experience designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical workstreams. Minimum 2 years of experience implementing end-to-end data pipelines for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration. Minimum 4 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases. Minimum 2 years of experience leading a team or workstream within a larger program. Experience collaborating with engineering, research, product, domain, client-facing, and cross-functional teams across multiple time zones. Required Skills/ Experience Hands-on experience building Azure-based knowledge engineering solutions with Azure Data Lake Storage, Azure Data Factory, Azure Synapse/Fabric, Azure AI Search, Azure OpenAI, Azure Machine Learning, AKS, Microsoft Purview, Entra ID, APIs, and Azure Monitor. Strong Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, and ETL/ELT pipeline tooling. Practical knowledge of NLP and search techniques including entity extraction, entity resolution, semantic search, prompt engineering, LLM grounding, and enterprise-scale LLM applications. Ability to design and implement scalable graph ingestion, ontology/schema pipelines, semantic layers, vector search/retrieval patterns, RAG pipelines, and governed knowledge services. Strong collaboration, technical leadership, delivery ownership, documentation, and stakeholder communication skills. Good to Have Skills 2+ years of hands-on experience with cloud platforms, with Azure specialization and exposure to AWS or GCP in multi-cloud environments. Azure certifications such as Azure Solutions Architect Expert, Azure Data Engineer, Azure AI Engineer, or related credentials. Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, including domain ontologies, data models, compliance needs, and knowledge-driven use cases. External client-facing consulting experience, proposal support, solution shaping, or pre-sales exposure. Advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline broad exposure to diverse ML techniques and agentic systems.
Required Skills/ Experience Hands-on experience building Azure-based knowledge engineering solutions with Azure Data Lake Storage, Azure Data Factory, Azure Synapse/Fabric, Azure AI Search, Azure OpenAI, Azure Machine Learning, AKS, Microsoft Purview, Entra ID, APIs, and Azure Monitor. Strong Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, and ETL/ELT pipeline tooling. Practical knowledge of NLP and search techniques including entity extraction, entity resolution, semantic search, prompt engineering, LLM grounding, and enterprise-scale LLM applications. Ability to design and implement scalable graph ingestion, ontology/schema pipelines, semantic layers, vector search/retrieval patterns, RAG pipelines, and governed knowledge services. Strong collaboration, technical leadership, delivery ownership, documentation, and stakeholder communication skills. Good to Have Skills 2+ years of hands-on experience with cloud platforms, with Azure specialization and exposure to AWS or GCP in multi-cloud environments. Azure certifications such as Azure Solutions Architect Expert, Azure Data Engineer, Azure AI Engineer, or related credentials. Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, including domain ontologies, data models, compliance needs, and knowledge-driven use cases. External client-facing consulting experience, proposal support, solution shaping, or pre-sales exposure. Advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline broad exposure to diverse ML techniques and agentic systems. 15 years full time education
Project Role : Data Architect Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration. Must have skills : Microsoft Azure Data Services Good to have skills : Data Engineering, Graph Databases Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary:
Summary: AI Powered Tech Talent Role Summary / Description Senior Engineer role in Knowledge Engineering focused on designing, building, and leading significant workstreams for enterprise-scale Knowledge Graph, semantic layer, ontology, and AI knowledge solutions on Microsoft Azure. In this role, the successful candidate leads a substantial knowledge engineering scope within a large program, translates real-world business problems into scalable AI and KG solutions, guides technical direction, and contributes to thought leadership, reusable assets, and delivery standards. The role must bring relevant industry experience across domains such as BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, applying semantic AI, knowledge graphs, LLM grounding, data pipelines, and cloud-native engineering patterns to deliver measurable client value. Key Responsibilities Engineer Azure-based knowledge engineering solutions using services such as Azure Data Lake Storage, Azure Data Factory, Azure Synapse/Fabric, Azure AI Search, Azure OpenAI, Azure Machine Learning, AKS, Microsoft Purview, Entra ID, APIs, and Azure Monitor. Build graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on Azure while improving performance, reliability, scalability, and cost efficiency. Lead the build of Knowledge Graph solutions that transform client data architecture within a large program scope. Direct the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions, ensuring all components work together seamlessly. Work with project leaders, delivery leads, client stakeholders, architects, data engineers, AI engineers, product teams, and domain SMEs to create standout graph-powered data and AI offerings. Develop strong client relationships, earn trust as a key advisor, and explain the business value of semantic layer, ontology, and knowledge graph solutions. Make the business case for the recommended semantic layer solution and contribute meaningfully to sales, pre-sales, estimation, proposal inputs, and solution shaping activities. Provide thought leadership on technology trends, innovation opportunities, limitations, risks, delivery concerns, and practical adoption of knowledge graphs, semantic AI, LLM grounding, RAG, and agentic systems. Design, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns, while guiding teams on data model quality and implementation methods. Lead a team or workstream within a larger program, mentor engineers, review design/code/configuration, and guide adoption of new methodologies, model building techniques, and algorithms. Collaborate across business and technical teams to drive end-to-end delivery for the assigned scope and demonstrate value to business and technology stakeholders.
Key Responsibilities Engineer Azure-based knowledge engineering solutions using services such as Azure Data Lake Storage, Azure Data Factory, Azure Synapse/Fabric, Azure AI Search, Azure OpenAI, Azure Machine Learning, AKS, Microsoft Purview, Entra ID, APIs, and Azure Monitor. Build graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on Azure while improving performance, reliability, scalability, and cost efficiency. Lead the build of Knowledge Graph solutions that transform client data architecture within a large program scope. Direct the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions, ensuring all components work together seamlessly. Work with project leaders, delivery leads, client stakeholders, architects, data engineers, AI engineers, product teams, and domain SMEs to create standout graph-powered data and AI offerings. Develop strong client relationships, earn trust as a key advisor, and explain the business value of semantic layer, ontology, and knowledge graph solutions. Make the business case for the recommended semantic layer solution and contribute meaningfully to sales, pre-sales, estimation, proposal inputs, and solution shaping activities. Provide thought leadership on technology trends, innovation opportunities, limitations, risks, delivery concerns, and practical adoption of knowledge graphs, semantic AI, LLM grounding, RAG, and agentic systems. Design, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns, while guiding teams on data model quality and implementation methods. Lead a team or workstream within a larger program, mentor engineers, review design/code/configuration, and guide adoption of new methodologies, model building techniques, and algorithms. Collaborate across business and technical teams to drive end-to-end delivery for the assigned scope and demonstrate value to business and technology stakeholders.
Required Qualifications Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field. Minimum 3 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation. Minimum 3 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation. Minimum 3 years of experience designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical workstreams. Minimum 2 years of experience implementing end-to-end data pipelines for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration. Minimum 4 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases. Minimum 2 years of experience leading a team or workstream within a larger program. Experience collaborating with engineering, research, product, domain, client-facing, and cross-functional teams across multiple time zones.
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Yes. We’ve followed 25 other Data Architect jobs there since October 2026. All 25 are still up.
Large employers often post one title for several teams, so these can be separate openings.
New Data Leadership postings at the employers we follow are about the same over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
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