[Job - 30900] Master Data Developer (Amazon Neptune), Colombia

1 нед. назад

ColombiaLATAMRemote

amazon neptunegraph databaseschema designdata ingestionentity resolutionquery optimization

Graph Database Developer working on AI-powered fashion advisory platform, responsible for graph data layer design and optimization.

Schema Design : Define node labels and edge types that represent the relationships between designers, garments, style attributes, trends, occasions, and editorial articles.
Entity Ingestion : Build workflows that consume structured JSON payloads from the upstream data pipeline and load them into the graph database in batch mode.
Probabilistic 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.
Editorial 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.
Query Optimization : Design and tune openCypher traversal queries, including multi-hop queries, to return relevant results in under three seconds for downstream Amazon Bedrock Agents.
Cross-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.
Technical Validation : Support technical interviews and validation of new team members joining the graph database workstream, as needed.

Advanced English proficiency (C1 or above), with autonomy to communicate directly with international stakeholders
Solid experience with Amazon Neptune and the property graph data model
Experience writing and optimizing openCypher queries, including multi-hop traversals under latency constraints
Experience designing graph schemas, evaluating trade-offs between different modeling approaches
Experience building entity resolution logic, including fuzzy matching, scoring algorithms, and deduplication across data sources
Strong Python skills for data engineering, including batch processing of structured data payloads
Ability to work independently in a fast-paced, staff augmentation setting with an established client team

Prior experience in fashion, retail, or e-commerce catalogs
AWS certification in databases or data analytics
Experience integrating graph databases with generative AI agents (for example, Amazon Bedrock Agents)
Familiarity with recommendation systems or ranking/weighting logic for content surfacing

At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
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.
The 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.