Data & AI Engineer

Quantiphi

. Build and modernize enterprise data and AI ecosystems .

Posted 9/17/2026contractRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and modernizing enterprise data ecosystems, implementing scalable data and AI pipelines, and integrating advanced AI solutions. Proficient in data engineering practices, including real-time streaming, data quality frameworks, and graph database technologies.

Highest-signal resume keywords

Data Engineering ExperienceApache Kafka ExpertiseGraph Database ProficiencyPython ProgrammingRAG Architecture Implementation

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data Pipeline DevelopmentReal-Time StreamingData Quality FrameworksGraph Database QueriesDimensional ModelingChunking TechniquesEmbedding StrategiesAI/ML IntegrationDataOpsCI/CD

Soft Skills

LeadershipStakeholder CommunicationCross-Functional CollaborationMentoring

Tools & Technologies

AWSAzureGCPApache KafkaAWS KinesisSpark StreamingNeo4jAmazon Neptune

Industry Keywords

Enterprise Data EcosystemsFinancial Industry DataSemantic Layer IntegrationGenAI SolutionsAgentic AI

Tech Stack

Tools & technologies

ApacheAWSAzureCloudGoogle Cloud PlatformJavaKafkaNeo4jPythonScalaSparkSQL

About the role

Key responsibilities & impact

Build and modernize enterprise data and AI ecosystems
Design, build, and maintain scalable real-time streaming pipelines
Implement data and AI pipelines for structured, semi-structured, and unstructured data supporting AI and Agentic solutions
Prepare data using extraction, chunking, embedding, and grounding strategies
Design data domains and data products for reporting, data science, AI/ML, and analytics
Design and implement Retrieval-Augmented Generation (RAG) architectures integrated with enterprise data infrastructure
Lead prototypes, experiments, and recommendations involving GenAI technologies
Model domain entities, relationships, and business logic in knowledge graphs
Integrate multi-source data with canonical representation and semantic consistency
Develop and validate synthetic data workflows for agent evaluation
Implement AI-driven data engineering productivity improvements and automated data quality frameworks
Design scalable semantic layers and real-time analytics capabilities for conversational analytics
Integrate semantic layers with AI/LLM platforms for secure, low-latency, context-rich data access
Monitor, alert, and manage incidents to ensure pipeline and system reliability, availability, and scalability
Implement redundancy, fault tolerance, and disaster recovery strategies
Collaborate with DevOps and infrastructure teams on deployment, operation, and maintenance
Mentor junior team members and lead communities of practice
Develop and optimize graph database queries, including Cypher and SPARQL
Design and apply GenAI solutions for insurance-specific data use cases
Partner with architects and stakeholders to implement the vision for AI and data pipelines

Requirements

What you’ll need

6+ years of hands-on data engineering experience building large-scale, complex enterprise data ecosystems on cloud platforms (AWS, Azure, or GCP)
Deep technical expertise in Apache Kafka, AWS Kinesis, Spark Streaming, and distributed processing frameworks
Proven experience with RAG architectures, vector search systems, chunking/embedding techniques, and LLM/Agentic AI data pipelines
Experience with graph databases such as Neo4j and Amazon Neptune
Experience with Cypher, SPARQL, or Gremlin
Proficiency in domain-driven data design, dimensional modeling, semantic layer integration, and reusable data products
High proficiency in Python, Scala, or Java, alongside SQL, DataOps, CI/CD, and containerized deployments
Experience handling complex, multi-structured financial-industry domain data strongly preferred
Exceptional leadership, stakeholder communication, and cross-functional collaboration skills
Track record of mentoring team members

Benefits

Comp & perks

Exposure to working with Fortune 500 companies and innovative market disruptors
Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud
Energetic team of highly dynamic and talented individuals
Opportunity to work at an AI-first digital transformation and engineering company
Contract (C2C) engagement