Senior Data Engineer

(RED)

. Own and evolve the technical direction of the data engineering platform .

Posted 10/14/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and evolving modern data platforms, with a strong focus on AI-driven development workflows and data observability. Proven ability to lead technical direction and implement engineering standards while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Data EngineeringAI-Driven Development WorkflowsSQLPythonCI/CD Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DesignData ArchitectureVersion ControlAutomated TestingData Quality ImprovementCloud-Based Data PlatformsMLOps PracticesObservabilityAI Coding ToolsDataOps Practices

Soft Skills

Stakeholder CommunicationCross-Functional CollaborationLeadershipAutonomy

Tools & Technologies

GCPBigQueryDbtCloud RunKubernetesBitbucket PipelinesClaude CodeCursorCodexScala

Industry Keywords

Data EngineeringAIAnalyticsMachine LearningProduction Data SystemsModern Data StackTravel IndustryHospitalityConsumer PlatformsPersonalization

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformKubernetesMavenPythonScalaSQL

About the role

Key responsibilities & impact

Own and evolve the technical direction of the data engineering platform
Define platform architecture, standards, and long-term platform evolution
Design and guide scalable, reliable data pipelines and platform capabilities for analytics and AI
Lead modernization toward more scalable, managed data platform solutions
Champion AI-assisted engineering and define how AI-generated code is reviewed, tested, and deployed
Design and build an end-to-end AI-driven development pipeline connecting planning, coding, testing, code review, CI/CD, and deployment
Define and enforce version control, code review, automated testing, and CI/CD standards
Build and improve observability across production data environments, including monitoring, alerting, logging, and lineage
Improve platform reliability, testing practices, and data quality
Create reusable frameworks, architecture patterns, and best practices
Partner with machine learning and technical teams to support AI platform enablement and shape MLOps practices
Collaborate with Data Engineering, Analytics Engineering, and business stakeholders
Contribute hands-on to foundational services, frameworks, and platform capabilities
Perform all other duties assigned

Requirements

What you’ll need

10+ years of experience in data engineering or related data platform roles
Experience designing and evolving modern data platforms, pipelines, and architecture
Strong SQL and Python skills
Hands-on, daily use of AI coding tools such as Claude Code, Cursor, or Codex
Track record of driving AI coding tool adoption across an engineering team
Experience designing or building AI-driven development workflows integrating AI agents and coding tools into testing, code review, and CI/CD
Git-based version control, code review, and automated unit, integration, and data quality testing
Experience building and maintaining CI/CD pipelines for data and platform code
Familiarity with Scala, Maven, and Bitbucket Pipelines
Strong experience implementing production data observability, including monitoring, alerting, logging, and lineage
Proven ability to improve reliability and data quality in production data systems
Experience with cloud-based data platforms and managed services across a modern data stack
Experience establishing engineering standards, architecture patterns, and reusable technical frameworks
Experience enabling AI use cases through strong data engineering foundations
Ability to operate independently and lead technical direction with a high degree of autonomy
Currently residing full-time in one of: Florida, Georgia, Illinois, New York, North Carolina, Pennsylvania, Tennessee, or Texas
Preferred: GCP, including BigQuery
Preferred: dbt or other modern data transformation frameworks
Preferred: Ops or DevOps knowledge, including Cloud Run and Kubernetes
Preferred: Exposure to MLOps concepts and collaboration with machine learning engineers
Preferred: Experience building AI agents and agentic workflows, including MCP servers
Preferred: Familiarity with RAG, embeddings, vector search, and evaluating and monitoring AI systems in production
Preferred: DataOps practices and modern platform operating models
Preferred: Experience in travel, hospitality, consumer platforms, or recommendations and personalization
Preferred: Strong stakeholder communication and cross-functional collaboration skills

Benefits

Comp & perks

Fully remote work arrangement
Occasional in-person meetings or team gatherings
Inclusive workplace environment
Equal opportunity employment