Staff Data Engineer

About Grubhub

At Grubhub, we believe food is more than just a meal: It’s a source of discovery, connection, and pure enjoyment. There’s a time and place for every type of dish, from hidden neighborhood gems to tried-and-true favorites, and we exist to connect people with the food they love in all the ways they like to dig in. We’ve been at it since 2004, but now, as part of Wonder, Grubhub is operating with a renewed sense of momentum and the high-velocity energy of a powerhouse startup.

As a leading U.S. ordering and delivery marketplace, we feature over 415,000 merchants in more than 4,000 cities, creating the ultimate food experience by elevating online ordering through innovative restaurant technology, easy-to-use platforms, and an improved delivery experience. We are constantly finding new ways to innovate—from integrated grocery delivery with groceries powered by Instacart to exclusive loyalty programs.

Join our team, based out of New York City and Chicago, and help us give our diners the exceptional value they deserve.

About The Opportunity

At Grubhub, we treat Data as a Product: a valuable asset and a critical part of how we make decisions. The mission of the Grubhub Data Platform team is to democratize data and to provide a cost-efficient, observable and reliable data platform. That platform lets teams move faster while using data safely to generate actionable insights and product features.

We are looking for a Staff Data Engineer to provide technical leadership across the core data systems and domain data products that power Grubhub's business. This is a hands-on technical leadership role. You will own the architecture of multiple connected pipelines and data products, and you will be accountable for how they fit together, not only for any single pipeline. You will set technical direction alongside other staff engineers, product managers and platform partners.

You will raise the bar on how the organization builds, validates and operates data, and you will mentor the engineers around you through design reviews, code reviews and direct feedback.

The Impact You Will Make

Duties will include, but are not limited to:

Own the architecture of critical data pipelines and domain data products end to end (ingestion, transformation, modeling and serving), and make the cross-system design calls.

Set technical direction and standards for data engineering across multiple teams: data modeling, CI/CD automated testing, data validation, data observability, SLOs for tier-1 datasets.

Write performant, concise and secure code on the most complex and business-critical problems, and lead design and code reviews beyond your own team.

Partner with product, analytics and platform teams to standardize metrics and publish trusted golden datasets for reporting, experimentation and ad-hoc analysis.

Proactively find and manage technical debt, cost inefficiency and reliability risk across the data stack before they become urgent.

Evaluate new technologies and architectural patterns rigorously: adopt what actually fits our scale and needs, and say no to what does not.

Mentor senior and mid-level engineers, take part in hiring, and grow the team's technical depth so that no critical system depends on a single person.

Translate technical trade-offs into business terms for stakeholders up to leadership, and document design rationale clearly enough that decisions outlive the people who made them.

Educate others and promote data engineering best practices to build a strong data culture across the engineering organization.

What You Bring to the Table

• Bachelor's Degree in a science, programming or engineering related field, or equivalent experience
• 6+ years of experience in data engineering, with a strong foundation in distributed systems and software engineering.
• Deep expertise in dimensional data modeling (star/snowflake schemas, fact and dimension design, slowly changing dimensions) and in designing reusable, well-governed data products for analytics at scale.
• Advanced SQL and hands-on experience with large-scale distributed processing frameworks such as Spark (PySpark/Spark SQL), Hive, Trino and Flink, including performance tuning and cost optimization of large workloads with high uptimes.
• Multi-cloud data platform experience across AWS (e.g. S3, EMR) and GCP (e.g. BigQuery, GCS, Dataproc). Experience migrating data pipelines and warehouses to GCP/BigQuery is highly valued.
• Experience building batch and streaming pipelines with workflow orchestration tools (e.g. Airflow, Azkaban), and a passion for event-driven architecture.
• Strong data quality engineering practices: CI/CD automated testing, data validation, data observability and data privacy.
• Solid software engineering fundamentals: Python (Java/Scala a plus), data structures and algorithms, clean and testable code, and code reviews.
• Demonstrated technical leadership at staff scope: setting technical direction across multiple teams or systems, driving architecture reviews beyond your own team, and influencing decisions without direct authority.
• You communicate clearly with both technical and non-technical audiences, up to and including executive stakeholders.
• You are pragmatic without compromising on software quality and standards.
• Got These? Even Better
• Finance and accounting domain experience: pipelines for orders, payments and accounting, built in partnership with FP&A, Accounting to define and certify financial metrics (e.g. GMV, net revenue).
• Reconciliation pipelines (order to payment to ledger) that support month-end close, revenue and accruals.
• SOX-compliant data pipelines: change management, access controls, data lineage and auditability.
• New York Base Salary: $208,500 - $225,000.
• Illinois Base Salary: $187,500 - $202,500.
• Wonder uses geographic-specific salary structures, which means the salary offered may vary depending on where the job is located. The final salary offer will take into account various factors, such as the candidate's skills, education, training, credentials, and experience.