Staff Data Engineer

Wonder

3d • $187,500 - $225,000

About The Position

The Staff Data Engineer provides hands-on technical leadership across the core data systems and domain data products that power Grubhub’s business. The role owns the architecture of multiple connected pipelines and data products end to end, including ingestion, transformation, modeling, and serving, and makes cross-system design decisions. The engineer sets technical direction with staff engineers, product managers, and platform partners, and improves how the organization builds, validates, and operates data. The role also mentors engineers through design reviews, code reviews, and direct feedback.

The Grubhub Data Platform team aims to democratize data by providing a cost-efficient, observable, and reliable platform that enables teams to move faster and use data safely for actionable insights and product features. This position partners with product, analytics, and platform teams to standardize metrics and publish trusted datasets for reporting, experimentation, and analysis. It also addresses technical debt, cost inefficiency, and reliability risks; evaluates technology choices; contributes to hiring and team development; and communicates technical trade-offs and design rationale to stakeholders, including leadership.

Requirements

• Bachelor’s degree in a science, programming, or engineering-related field, or equivalent experience.
• At least 6 years of data engineering experience, with a strong foundation in distributed systems and software engineering.
• Deep expertise in dimensional data modeling, including star and snowflake schemas, fact and dimension design, and slowly changing dimensions.
• Experience designing reusable, well-governed data products for analytics at scale.
• Advanced SQL skills and hands-on experience with large-scale distributed processing frameworks such as Spark (PySpark/Spark SQL), Hive, Trino, and Flink.
• Experience tuning performance and optimizing costs for large workloads with high uptimes.
• Multi-cloud data platform experience across AWS, such as S3 and EMR, and GCP, such as BigQuery, GCS, and Dataproc.
• Experience building batch and streaming pipelines with workflow orchestration tools such as Airflow or Azkaban.
• Strong data quality engineering practices, including CI/CD automated testing, data validation, data observability, and data privacy.
• Solid software engineering fundamentals, including Python, data structures and algorithms, clean and testable code, and code reviews.
• Demonstrated staff-scope technical leadership, including setting direction across multiple teams or systems, leading architecture reviews beyond one’s team, and influencing decisions without direct authority.
• Clear communication with technical and non-technical audiences, including executive stakeholders.
• A pragmatic approach that maintains software quality and standards.

Nice To Haves

• Experience migrating data pipelines and warehouses to GCP/BigQuery.
• Java or Scala experience.
• Finance and accounting domain experience, including developing pipelines for orders, payments, and accounting in partnership with FP&A and Accounting to define and certify financial metrics such as GMV and net revenue.
• Experience building reconciliation pipelines from order to payment to ledger to support month-end close, revenue, and accruals.
• Experience with SOX-compliant data pipelines, including change management, access controls, data lineage, and auditability.

Responsibilities

• Own the end-to-end architecture of critical data pipelines and domain data products, including ingestion, transformation, modeling, and serving.
• Make cross-system design decisions and set technical direction and standards for data engineering across multiple teams.
• Establish standards for data modeling, CI/CD automated testing, data validation, data observability, and SLOs for tier-1 datasets.
• Write performant, concise, and secure code for complex, business-critical problems.
• Lead design and code reviews beyond the immediate team.
• Partner with product, analytics, and platform teams to standardize metrics and publish trusted datasets for reporting, experimentation, and ad-hoc analysis.
• Proactively identify and manage technical debt, cost inefficiency, and reliability risks across the data stack.
• Evaluate technologies and architectural patterns, adopting those that fit the organization’s scale and needs.
• Mentor senior and mid-level engineers, participate in hiring, and build the team’s technical depth to avoid reliance on a single person for critical systems.
• Translate technical trade-offs into business terms for stakeholders, including leadership, and document design rationale.
• Educate colleagues and promote data engineering best practices across the engineering organization.

Benefits

• Competitive compensation package with equity.
• 401(k).
• Choice of medical, dental, and vision plans.
• Company-paid short- and long-term disability coverage.
• Paid time off, including flexible time off for exempt employees and paid vacation for non-exempt employees.
• Paid sick leave in compliance with applicable law.
• Paid parental leave.
• Discounted meals and exclusive perks across the Wonder family of brands. Availability and eligibility vary by employment classification and location.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior