{"id":4150990,"url":"https://alion.io/job/accenture-data-architect-25","title":"Data Architect","company":{"id":271,"name":"Accenture","domain":"accenture.com","url":"https://alion.io/company/accenture","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":2137,"ghost_share":0.001,"stale_share":0.182,"repost_share":0.007,"time_to_fill_p50_days":5,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AI Agents","optional":true},{"name":"Airflow","optional":true},{"name":"Amazon Neptune","optional":true},{"name":"Amazon Redshift","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Apache NiFi","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Azure","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GCP","optional":true},{"name":"IAM","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LLM","optional":true},{"name":"Machine Learning","optional":true},{"name":"Neo4j","optional":true},{"name":"NLP","optional":true},{"name":"OpenSearch","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Semantic Search","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-11-11T00:00:00Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-12T09:11:41Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Project Role : Data ArchitectProject Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.\nMust have skills : AWS BigData\nGood to have skills : NA\nMinimum 12 year(s) of experience is required\nEducational Qualification : 15 years full time education\nSummary:\nAI Powered Tech Talent\nRole Summary / Description\nSenior 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 Amazon Web Services (AWS). 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.\nKey Responsibilities\nEngineer AWS-based knowledge engineering solutions using services such as Amazon Neptune, S3, Glue, Lambda, EMR, Redshift, Athena, OpenSearch, Bedrock, SageMaker, IAM, Step Functions, APIs, and monitoring/security services.\nBuild graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on AWS while improving performance, reliability, scalability, and cost efficiency.\nLead the build of Knowledge Graph solutions that transform client data architecture within a large program scope.\nDirect the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions, ensuring all components work together seamlessly.\nWork 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.\nDevelop strong client relationships, earn trust as a key advisor, and explain the business value of semantic layer, ontology, and knowledge graph solutions.\nMake the business case for the recommended semantic layer solution and contribute meaningfully to sales, pre-sales, estimation, proposal inputs, and solution shaping activities.\nProvide 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.\nDesign, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns, while guiding teams on data model quality and implementation methods.\nLead 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.\nCollaborate across business and technical teams to drive end-to-end delivery for the assigned scope and demonstrate value to business and technology stakeholders.\nRequired Qualifications\nBachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.\nMinimum 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.\nMinimum 3 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.\nMinimum 3 years of experience designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical workstreams.\nMinimum 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.\nMinimum 4 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.\nMinimum 2 years of experience leading a team or workstream within a larger program.\nExperience collaborating with engineering, research, product, domain, client-facing, and cross-functional teams across multiple time zones.\nRequired Skills/ Experience\nHands-on experience building AWS-based knowledge engineering solutions with Amazon Neptune, S3, Glue, Lambda, EMR, Redshift, Athena, OpenSearch, Bedrock, SageMaker, IAM, Step Functions, and cloud-native APIs/monitoring services.\nStrong Python experience with frameworks and tools such as TensorFlow, PyTorch, PySpark, SQL, SPARQL, SHACL, Apache Airflow, Apache NiFi, and ETL/ELT pipeline tooling.\nPractical knowledge of NLP and search techniques including entity extraction, entity resolution, semantic search, prompt engineering, LLM grounding, and enterprise-scale LLM applications.\nAbility to design and implement scalable graph ingestion, ontology/schema pipelines, semantic layers, vector search/retrieval patterns, RAG pipelines, and governed knowledge services.\nStrong collaboration, technical leadership, delivery ownership, documentation, and stakeholder communication skills.\nGood to Have Skills\n2+ years of hands-on experience with cloud platforms, with AWS specialization and exposure to Azure or GCP in multi-cloud environments.\nAWS certifications such as Solutions Architect, Data Engineer, Machine Learning/AI Specialty, or related credentials.\nIndustry 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.\nExternal client-facing consulting experience, proposal support, solution shaping, or pre-sales exposure.\nAdvanced 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.\n15 years full time educationAbout Accenture\nAccenture 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 atwww.accenture.com\nEqual Employment Opportunity Statement\nWe 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, militaryveteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicablelaw. 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