Meet Saumya, a Data Engineer II on our Destination Attribute Store team. Her work helps transform raw information, from travel location details and local events, to predictive booking trends, into actionable insights that bring destinations to life. From forecasting weekly booking values to curating event data that informs pricing and planning, Saumya’s role is all about turning complexity into clarity.

We caught up with Saumya, to learn what her advice is for how to excel in a role within data engineering at Expedia Group. Find out her takeaways, below.

Saumya Shalini | Data Engineer II | Bangalore, India

Tell us about your team and role

I work as a Data Engineer II in the Destination Attribute Store (DAS) team. We organize and manage data from various external and internal sources to provide comprehensive, destination-grain datasets that support all Expedia Group brands and business lines. Whenever you search for a city, beach, or mountain getaway on the Expedia app or site, our data helps bring that destination to life. We collect and organize important details, like where a place is located, what it offers, when people are travelling there and what events are taking place. We even have forecast data predicting expected weekly gross booking value (GBV) for the next year. We turn all of this into easily consumable insights for other teams to use. In short, our team helps understand the world of travel through destination data.

What Expedia Group products and services interest you the most from a technology perspective?

Across Expedia Group, the most compelling technology recently, for me, is the acceleration of GenAI capabilities. We’re moving from static reporting to agentic copilots that perform retrieval-augmented reasoning, orchestrate tools, and write back into operational systems. A mature stack of evaluations, guardrails, and observability lets us ship quickly while preserving safety, reliability, and measurable quality.

Within our team, two launches exemplify this momentum. Travel Corridors delivers calibrated GBV forecasts at POSA/POSU granularity using ML models, with actionable insights. Destination Events provides comprehensive data at the destination, covering concerts, sporting events, natural disasters, offering timely context that can inform planning, pricing, and customer communications. These products translate relevant data into operational levers demonstrating technical depth and clear business impact.

What are your tips for how to succeed in a data engineering role?

These five practices have helped me deliver stable pipelines, clear insights, and visible business value.

1. Own the lifecycle, not a tool: The strongest data engineers take ownership from intake to impact. Translate a requirement into a measurable pipeline and a decision. Start with a measurable target: latency, cost ceiling, data volume, and quality thresholds. Design the path, deliver a minimal working version quickly, then iterate with feedback. Focus on the basics, the results, a reliable dataset and the decision it enables.
   
   
2. Attention to detail: Trust is earned when consumers know what a dataset means, how fresh it is, and what will change. Define data contracts with producers: fields, types, semantics, SLAs, and change policy. Partition data and keep checkpoints to enable backfills without downtime. Enforce quality gates before publish: schema drift checks, row-count expectations, null and distribution thresholds. Maintain documentation and lineage with clear ownership and review cadence. Aim for fewer incidents, faster onboarding of analysts and ML teams, and predictable evolution.
   
   
3. Master the fundamentals: Frameworks change, but core skills compound. Get strong in SQL and storage; orchestration; and compute for both batch and streaming. Build operational fluency in Git, Bash, containers, Linux basics, IAM, cost management, and PII privacy controls. Try to fix performance and cost issues at the root instead of chasing new tools.
   
   
4. Make skill-building a habit: Stay informed on relevant topics, designate some time on a weekly cadence; practice by shipping small end-to-end pipelines on public datasets with prod-like tooling . Watch meetups, talk demos and conferences. Aim for a steady, visible path from idea to reliable demo, staying up to date on relevant advancements in the field and a credible portfolio.
   
   
5. Communicate with clarity and accountability: Ask questions. Try to understand the business need first. Write concise design docs: problem, options, trade‑offs, estimated cost, risks, rollout and rollback plans. Send regular health updates: freshness, success rates, SLO adherence, upcoming changes. Use data to set scope: show latency/cost implications and propose staged delivery. Keep runbooks and post‑incident reviews; capture lessons and automate the fix where possible. Stakeholders trust timelines, and your work is then easier to review, maintain, and promote.

Why would you recommend a career at Expedia Group?

At Expedia Group amazing people and meaningful travel opportunities meet real, scalable tech. Here you’ll build practical innovation that millions feel, from powerful APIs and smart algorithms to creating new ideas that enhance the travel experience, all driven by data. Learning is built in, with clear feedback, mentorship, and hackathons. The company is transforming at pace and quickly adapts to the latest tech, so there’s real ownership and room to experiment. If you want to have continuous growth, and make an impact on how people explore the world, Expedia Group is an easy yes!

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