Data Engineer

Kyndryl

AI Data Engineer architecting high-performance data infrastructure at Kyndryl, focusing on autonomous systems. Designing pipelines for Retrieval-Augmented Generation and ensuring data quality and performance.

Posted 8/1/2026full-timeLima • 🇵🇪 PeruMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and scaling data pipelines for Retrieval-Augmented Generation (RAG) while ensuring the health and performance of vector databases. Proficient in building automated data guardrails and deploying CI/CD pipelines for data infrastructure.

Highest-signal resume keywords

Data MiningData StorageETL ProcessesData Pipeline DevelopmentData Modelling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data MiningData StorageETL ProcessesData Pipeline DevelopmentData ModellingRelational DatabasesNoSQL DatabasesPostgreSQLMongoDBCI/CD Pipelines

Soft Skills

Problem-SolvingAnalytical SkillsCritical ThinkingAttention to DetailCommunication Skills

Tools & Technologies

PineconeMilvusWeaviateGlueDatabricksSynapseDataprocGitHubVisual Studio

Certifications & Qualifications

Open Certified Technical Specialist with Data Engineering SpecializationCloud Platform Certification

Industry Keywords

Retrieval-Augmented GenerationVector InfrastructureKnowledge GraphsSemantic LayersAutomated Data GuardrailsCloud Modernization

Tech Stack

Tools & technologies

CloudETLMongoDBNoSQLPostgres

About the role

Key responsibilities & impact

Architect for RAG: Design and scale the pipelines for Retrieval-Augmented Generation (RAG)
Scale vector infrastructure: Responsible for the health and performance of vector databases (e.g., Pinecone, Milvus, or Weaviate)
Engineer semantic layers: Move beyond simple ETL to build knowledge graphs and semantic layers
Automate data excellence: Build automated data guardrails to detect noise, bias, or PII
Solve meaningful challenges: Serve as the bridge between raw data sources and deep technical AI work
Progress to production: Build, deploy, and maintain CI/CD pipelines for data infrastructure

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 (preferred)
Experience in Data Modelling (preferred)
Professional certification, e.g. Open Certified Technical Specialist with Data Engineering Specialization (preferred)
Cloud platform certification (preferred)
Understanding of social coding and Integrated Development Environments, e.g. GitHub and Visual Studio (preferred)
Degree in a scientific discipline, such as Computer Science, Software Engineering, or Information Technology (preferred)

Benefits

Comp & perks

Flexible working hours
Professional development opportunities
Be Well programs designed to support financial, mental, physical, and social health