Accenture
Global professional services and technology consulting
Knowledge Engineer
About the job
- A bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field is required.
- At least 6 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, schema design, ontology management, and knowledge graph curation is required.
- At least 6 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation is required.
- At least 4 years of experience designing and developing Knowledge Graph solutions and graph-based machine learning models across functional and technical workstreams is required.
- At least 3 years of experience implementing end-to-end data pipelines for artificial intelligence applications, especially large language model-enabled or enterprise knowledge applications, with hands-on design and configuration is required.
- At least 6 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases is required.
- At least 6 years of managerial or technical leadership experience leading teams and explaining the value of semantic layers and knowledge graphs to senior business and technology stakeholders is required.
- Experience contributing to sales, pre-sales, solution shaping, delivery leadership, stakeholder management, and enterprise data transformation programs is required.
- Deep knowledge of knowledge graph architecture, semantic modeling, ontology engineering, metadata management, data governance, graph curation, and graph-based artificial intelligence and machine learning patterns is required.
- Hands-on experience architecting Google Cloud Platform-based knowledge engineering solutions using BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI, Cloud Run, Pub/Sub, Identity and Access Management, application programming interfaces, and cloud security and monitoring services is required.
- Strong Python expertise and hands-on experience with PyTorch, TensorFlow, PySpark, Apache Airflow, Apache NiFi, SQL, SPARQL, SHACL, application programming interfaces, and extract, transform, load or extract, load, transform pipelines is required.
- Ability to design scalable graph ingestion, schema and ontology pipelines, semantic data products, vector search and retrieval patterns, retrieval-augmented generation grounding layers, and large language model-ready knowledge services is required.
- Strong architecture leadership across cloud integration, data pipelines, security, governance, observability, cost optimization, reusable assets, and production readiness is required.
- Design and structure knowledge frameworks that enable artificial intelligence systems to reason and make informed decisions.
- Capture and translate expert and unstructured knowledge into ontologies, knowledge graphs, and semantic models while ensuring accuracy and context for automation and insights.
- Apply advanced analytics on knowledge graphs to drive problem-solving and actionable insights.
- Define Google Cloud Platform reference architectures for knowledge graphs, semantic layers, and artificial intelligence knowledge applications using services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI, Cloud Run, Pub/Sub, Identity and Access Management, Cloud Logging and Monitoring, and graph and vector ecosystem services.
- Architect cloud-native data pipelines, graph ingestion, ontology management, vector and database integrations, application programming interface layers, large language model grounding patterns, and governed knowledge access models on Google Cloud Platform.
- Lead the complete Knowledge Graph and Knowledge Engineering solution scope that transforms data architecture for strategic, complex client programs.
- Own the design, development, and implementation of artificial intelligence, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions across the program.
- Partner with project leaders, delivery leads, senior client stakeholders, architects, product teams, data engineers, artificial intelligence engineers, and domain subject-matter experts to create graph-powered offerings.
- Develop trusted-advisor relationships with senior client stakeholders and make a business case for semantic layer, knowledge graph, and enterprise artificial intelligence knowledge architecture solutions.
- Lead architecture governance, design reviews, solution estimation, pre-sales support, proposal inputs, implementation planning, and technical risk management for complex knowledge engineering programs.
- Set standards for ontology design, semantic modeling, metadata management, data governance, lineage, knowledge graph curation, and reusable engineering patterns across programs.
- Build and mentor multidisciplinary teams, establish capability development plans, and guide delivery quality for knowledge engineers, data engineers, artificial intelligence engineers, and platform specialists.
- Drive thought leadership, innovation, reusable assets, accelerators, and modern methods around knowledge graphs, semantic artificial intelligence, large language model grounding, retrieval-augmented generation, agentic systems, and graph-based artificial intelligence patterns.
- Translate industry-specific business problems into scalable knowledge-driven architectures and reusable assets that advance the discipline beyond a single engagement.
- Practical experience with natural language processing techniques, search techniques, prompt engineering, entity extraction, entity resolution, semantic search, and enterprise-scale large language model applications is preferred.
- At least 5 years of hands-on experience with cloud platforms, with deep Google Cloud Platform specialization and working exposure to Amazon Web Services or Microsoft Azure in multi-cloud environments is preferred.
- Google Cloud Platform certifications such as Professional Cloud Architect, Professional Data Engineer, Professional Machine Learning Engineer, or related credentials are preferred.
- Industry experience in banking, financial services and insurance, healthcare, retail, telecommunications, manufacturing, energy, the public sector, or life sciences, including industry-specific ontologies, data models, compliance needs, and knowledge-driven use cases, is preferred.
- An advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline is preferred.
About the company
Accenture is a global professional services firm that helps companies navigate technology-driven change. It offers strategies and services across consulting, digital, technology, and operations, with a strong emphasis on cloud, artificial intelligence, security, and enterprise reinvention. Accenture works by delivering end-to-end solutions, combining advisory work with implementation, technology platforms, and managed services to transform how organizations operate and compete in today’s digital world. The company differentiates itself through its long history as a dedicated tech-advisory arm that gained independence in 2001, its scale, and its active acquisitions—particularly since 2013—to expand capabilities in digital, cloud, and security. Its goal is to help the world’s largest corporations rethink and reshape their operations to stay ahead of rapid technological shifts.
Company Size
10,001+
Company Stage
IPO
Headquarters
Dublin, Ireland
Founded
1989
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Simplify's Take
What believers are saying
- July 7, 2026 Google Cloud collaboration turns Accenture Edge into an AI production engine.
- June 2026 fiscal Q3 revenue reached $18.72 billion, while operating margin expanded to 17%.
- Accenture raised fiscal 2026 acquisition spending to $9 billion, buying Faculty and other specialists.
What critics are saying
- June 18, 2026 bookings fell 2%, signaling weaker demand visibility into fiscal 2027.
- September 17, 2026 DOJ settled Accenture Federal Services allegations for $25 million, pressuring federal work.
- AI tools from Google, OpenAI, and internal clients commoditize consulting; margins collapse by 2027.
What makes Accenture unique
- Accenture Edge launched June 23, 2026, packaging AI, cloud, and security for mid-market firms.
- June 2026 partnerships with Google Cloud and OpenAI deepen Accenture's enterprise AI distribution.
- Fiscal Q3 2026 bookings hit $19.3 billion across 104 billion-dollar client wins.
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Benefits
Health Insurance
Professional Development Budget
401(k) Retirement Plan
401(k) Company Match
Company News
Accenture has completed the acquisition of Faculty, a leading UK‑based AI company known for its deep technical expertise and pedigree in applying AI safely across public and private sectors to help clients improve services and deliver growth.
Accenture has agreed to acquire Whalar, a leading creator and social agency, from Whalar Group.
Accenture and Micron Technology present contrasting investment opportunities for 2026. Accenture, a global consulting firm, generated $69.7 billion in revenue during fiscal 2025, up 7.4% year-over-year, with net income of $7.8 billion. The company maintains conservative debt levels and strong client retention. Micron Technology, a memory chip manufacturer, saw revenue surge 48.9% to $37.4 billion in fiscal 2025, with net income of $8.5 billion—a sharp turnaround from a $5.8 billion loss in fiscal 2023. This growth reflects strong demand for AI-related high-bandwidth memory. Both companies face competitive pressures and geopolitical risks. Accenture contends with new AI regulations and ecosystem dependencies, whilst Micron faces pricing pressure from rivals and trade restrictions. Analysts favour Micron for its exposure to early-stage AI memory demand, despite cyclical risks. Accenture offers steady cash generation but faces softer near-term bookings.
Investors seeking financial stability often look to companies with more cash than debt, as these businesses have greater flexibility for growth investments, buybacks, and dividends. Intuitive Surgical holds $5.22 billion in net cash. The robotic surgery pioneer achieved 20.7% annual revenue growth over the past two years, whilst its earnings per share increased 17.4% annually over five years. IonQ maintains $2.06 billion in net cash, representing 14.1% of its market capitalisation. The quantum computing company posted 181% annual revenue growth over two years and expects 163% growth next year. Accenture has $1.78 billion in net cash. The professional services firm employs approximately 774,000 people across more than 120 countries. Companies with strong balance sheets can navigate uncertain markets whilst pursuing expansion opportunities without the burden of significant debt obligations.
Accenture trades 37% below its 52-week high after falling 27% over the past year. In fiscal Q3 2026, new bookings declined and some managed services deals slipped into fiscal 2027. The company generated $73.1 billion in revenue over the past twelve months, with operating margins widening to 17% in Q3. Managed services revenue grew 5% in local currency. In the first nine months of fiscal 2026, clients made 104 quarterly bookings exceeding $100 million, up 13% year-on-year. Consulting revenue grew just 1% in local currency in Q3. Management attributes the slowdown to the Middle East conflict affecting regional and discretionary spending. The CEO acknowledged that client budgets have not been growing, with spending simply being redirected rather than increased.