Data Engineer II

Expedia Expedia · Hospitality · Gurgaon, India

Data Engineer II role focused on building and maintaining scalable data pipelines and processing solutions for analytics, reporting, and product use cases. The role involves developing data models, ensuring data quality, and collaborating with cross-functional teams. Familiarity with AI/ML concepts and their application in data engineering is a plus.

What you'd actually do

  1. Design, build, and maintain scalable, reliable, and secure data pipelines and batch/streaming data processing solutions that support analytics, reporting, and product use cases across multiple domains.
  2. Develop, optimize, and maintain data models, schemas, and interfaces (including APIs and data contracts) that ensure data is well-structured, discoverable, and usable by downstream systems and stakeholders.
  3. Implement high-quality, well-tested, and observable data engineering solutions using approved Expedia Group technologies, following established coding standards, system design (including low-level design), and data modeling practices.
  4. Collaborate with product, analytics, and engineering partners to understand data requirements, translate them into technical solutions, and deliver high-impact datasets and services that enable data-driven decision making.
  5. Ensure end-to-end operational excellence of data services, including monitoring, alerting, performance tuning, cost optimization, and incident response for the data products and pipelines you own.

Skills

Required

  • data modeling
  • SQL
  • programming language for data engineering
  • system design
  • API design
  • data integration patterns
  • ownership of end-to-end components
  • modern data platforms
  • storage systems
  • processing frameworks
  • testing
  • code reviews
  • version control

Nice to have

  • designing and operating data pipelines and services at scale
  • optimization for performance, reliability, and cost
  • implementing robust data models and contracts
  • leading design discussions
  • observability and operational excellence for data systems
  • metrics
  • logging
  • alerting
  • runbook creation
  • participation in on-call or incident response
  • familiarity with AI-driven systems, tools, or workflows
  • applying AI/ML concepts to real world products
  • safely integrating and operating AI/ML‑enabled solutions
  • role-appropriate experience leveraging AI/ML in data engineering contexts
  • feature data pipelines for ML models
  • data preparation for training/serving
  • using AI-assisted tools to improve development productivity and data reliability

What the JD emphasized

  • scalable
  • reliable
  • secure
  • data pipelines
  • data processing
  • data models
  • data engineering solutions
  • operational excellence
  • data services
  • performance tuning
  • cost optimization
  • incident response
  • data platforms
  • large datasets
  • high-throughput environments
  • robust data models
  • system design
  • API interfaces
  • data architectures
  • observability
  • operational excellence
  • metrics
  • logging
  • alerting
  • runbook creation
  • incident response
  • data platforms
  • AI-driven systems
  • AI/ML concepts
  • AI/ML‑enabled solutions
  • AI/ML in data engineering contexts
  • feature data pipelines for ML models
  • data preparation for training/serving
  • AI-assisted tools