Data Engineer II - (Global Discovery, Consumer)

Job description

Deep analysis. Smarter systems. Real users. As a Data Engineer II , you’ll own the batch pipelines and data products behind Global Discovery, the search and recommendation services used across all Delivery Hero platforms. Batch pipeline ownership is moving to our squad, so you’ll take on a well-defined scope from day one and help build the Unified Data Layer that analysts and data scientists rely on every day. The Analytics Engineering squad is part of Data Engineering in Global Discovery: three Data Engineers and an Engineering Manager.

We own the data products behind Discovery analytics, from data quality monitoring to supporting recommendation and ML workflows across several data science teams. Now we’re building the Unified Data Layer as the single source of truth for analytics and data science, and taking full ownership of batch pipelines as our data platform team takes over stream processing. At Delivery Hero, you’ll shape the global consumer experience for millions of users.

Your work will have a direct impact on how we attract and retain customers, creating data-driven, personalized interactions that keep them coming back. As part of our Consumer Team, you’ll enhance customer satisfaction and drive global growth and profitability through innovative projects. Own a well-defined scope of batch pipelines end-to-end, building new Python workflows and reworking existing ones so they are more reliable, readable, and testable.

Shape how we use Airflow and dbt to define, build, and operate the Unified Data Layer, keeping BigQuery workloads efficient and cost-aware. Keep our workflows reliable using tiers, SLOs, and runbooks, and find and fix the root cause when something breaks in production. Own alerts and grow the maturity of our data quality checks, business-logic validation, and testing suite. Build self-serve tooling that lets analysts and data scientists create their own analytical layers with less effort.

Make our codebases and workflows native to agentic tooling, so coding agents speed up the team without lowering the quality bar.

Qualifications

3+ years in data engineering, building production data workflows primarily in Python. Advanced analytical/OLAP SQL, ideally in BigQuery, including tuning queries for performance and cost. Production experience with Airflow and dbt, including system reliability, data quality monitoring, and alerting. Daily use of coding agents (e.g. Claude Code) to scaffold DAGs and dbt models, with the rigor to verify what they produce (grain, joins, reconciliation) and full ownership of the result.

Solid engineering practices

unit and integration testing, thoughtful code reviews, observability, and code that anyone unfamiliar with the system can understand. Curiosity about the business behind the data, with the ability to dig into the logic, explain what the numbers mean, and work closely with analysts and data scientists. Nice to have Experience supporting recommendation, search, or ML data workflows. Exposure to stream processing (e.g. Flink, Kafka) and a clear view of batch vs. streaming trade-offs.

Experience building self-serve data tooling for analysts or other non-engineering users. Experience building your own agents or agent-based tooling for data engineering work.