Data & Platform Analyst – Real Estate Portfolio

Kyndryl

AI Data Engineer at Kyndryl architecting high-performance data infrastructure for autonomous systems. Building real-time pipelines for complex enterprise environments with a flexible, supportive culture.

Posted 6/30/2026full-timeRemote • 🇲🇽 MexicoMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudETLMongoDBNoSQLPostgres

About the role

Key responsibilities & impact

Architect for RAG: You’ll design and scale the pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings.
Scale vector infrastructure: You will be responsible for the health and performance of our vector databases (e.g., Pinecone, Milvus, or Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops.
Engineer semantic layers: Move beyond simple ETL to build knowledge graphs and semantic layers that provide agents with the necessary context to navigate complex infrastructure puzzles.
Automate data excellence: Using a keen eye for detail, you’ll build automated data guardrails to detect noise, bias, or PII (Personally Identifiable Information) before it reaches the model, ensuring our AI remains safe and impactful.
Solve meaningful challenges: Serve as the bridge between raw, messy data sources and deep technical AI work, identifying and resolving quality issues at the source.
Progress to production: With a well-defined methodology and software engineering prowess, you will build, deploy, and maintain the CI/CD pipelines for our data infrastructure, ensuring that our context window remains fresh and reliable.

Requirements

What you’ll need

Expertise in data mining, data storage and Extract-Transform-Load (ETL) processes
Experience in data pipelines development and tooling, e.g., Glue, Databricks, Synapse, or Dataproc
Experience with both relational and NoSQL databases, PostgreSQL, DB2, MongoDB
Excellent problem-solving, analytical, and critical thinking skills
Ability to manage multiple projects simultaneously, while maintaining a high level of attention to detail
Ability to communicate with both technical and non-technical colleagues, to derive and translate technical requirements from business needs
Experience working as a Data Engineer and/or in cloud modernization
Experience in Data Modelling, to create conceptual model of how data is connected and how it will be used in business processes
Professional certification, e.g. Open Certified Technical Specialist with Data Engineering Specialization
Cloud platform certification, e.g. AWS Certified Data Analytics – Specialty, Elastic Certified Engineer, Google Cloud Professional Data Engineer, or Microsoft Certified: Azure Data Engineer Associate
Understanding of social coding and Integrated Development Environments, e.g. GitHub and Visual Studio
Degree in a scientific discipline, such as Computer Science, Software Engineering, or Information Technology

Benefits

Comp & perks

Flexible working arrangements
Professional development
Wellness programs

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Data MiningETL ProcessesData Pipeline DevelopmentData ModelingVector EmbeddingsRelational DatabasesNoSQL DatabasesAutomated Data GuardrailsCI/CD PipelinesKnowledge Graphs

Soft Skills

Problem-SolvingAnalytical SkillsAttention to DetailCommunication SkillsCritical Thinking

Certifications

Open Certified Technical Specialist with Data Engineering SpecializationAWS Certified Data Analytics – SpecialtyElastic Certified EngineerGoogle Cloud Professional Data EngineerMicrosoft Certified: Azure Data Engineer Associate