{"id":119024,"url":"https://alion.io/job/ciandt-job-30900-master-data-developer-amazon-neptune-colombia","title":"Master Data Developer (Amazon Neptune), Colombia","company":{"id":957,"name":"CI&T","domain":"ciandt.com","url":"https://alion.io/company/ciandt","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":97,"open_postings":21,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":14,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Colombia"],"countries":["CO"],"hiring_countries":["CO"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"AWS","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-08-17T18:49:33Z","employer_posted_date":"2026-08-17","last_verified_at":"2026-10-28T09:04:04Z","board_verified":true,"closed_at":null,"days_open":71,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":71},"description":"As CI&T grows its Data & Analytics Center of Excellence, we seek a talented and experienced Graph Database Developer to join a specialized team building an AI-powered fashion advisory platform for a leading client in the fashion and retail industry. This role is central to turning unstructured editorial content into a structured knowledge graph that powers real-time, intelligent style recommendations for end users attending formal events.\nThe Graph Database Developer will work in a staff augmentation model, integrating with a cross-functional team that includes a Machine Learning Developer and a GenAI Agent Developer. This is a hands-on technical position requiring strong ownership of the graph data layer - from schema design through ingestion, entity resolution, and query performance - with direct impact on what the AI agent recommends to users.\nResponsibilities:\nSchema Design: Define node labels and edge types that represent the relationships between designers, garments, style attributes, trends, occasions, and editorial articles.\n\nEntity Ingestion: Build workflows that consume structured JSON payloads from the upstream data pipeline and load them into the graph database in batch mode.\n\nProbabilistic Entity Matching: Match extracted item mentions (for example, a bag described by type, color, and material) to the correct product node in a catalog of 50,000+ SKUs by scoring attribute overlap, assigning a match confidence, and creating the relationship only when it exceeds a defined threshold. Deduplicate items that appear inconsistently across different articles.\n\nEditorial Signal Weighting: Assign weights to relationships based on how prominently an item or trend was featured, distinguishing a dedicated feature from a passing mention. These weights determine what the AI agent surfaces first in its recommendations.\n\nQuery Optimization: Design and tune openCypher traversal queries, including multi-hop queries, to return relevant results in under three seconds for downstream Amazon Bedrock Agents.\n\nCross-Functional Collaboration: Work alongside the Machine Learning Developer and GenAI Agent Developer to align the graph structure with the retrieval and recommendation needs of the AI agent.\n\nTechnical Validation: Support technical interviews and validation of new team members joining the graph database workstream, as needed.\n\nRequirements:\nAdvanced English proficiency (C1 or above), with autonomy to communicate directly with international stakeholders\n\nSolid experience with the property graph data model\nExperience writing and optimizing openCypher queries, including multi-hop traversals under latency constraints\n\nExperience designing graph schemas, evaluating trade-offs between different modeling approaches\n\nExperience building entity resolution logic, including fuzzy matching, scoring algorithms, and deduplication across data sources\n\nStrong Python skills for data engineering, including batch processing of structured data payloads\n\nAbility to work independently in a fast-paced, staff augmentation setting with an established client team\n\nNice to Have:\nSolid experience with Amazon Neptune\nPrior experience in fashion, retail, or e-commerce catalogs\n\nAWS certification in databases or data analytics\n\nExperience integrating graph databases with generative AI agents (for example, Amazon Bedrock Agents)\n\nFamiliarity with recommendation systems or ranking/weighting logic for content 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