Senior Data Engineer – Martech

Glovo is part of the Delivery Hero Group, the world’s pioneering local delivery platform, our mission is to deliver an amazing experience—fast, easy, and to your door. We operate in around 65 countries worldwide. Headquartered in Berlin, Germany. Delivery Hero has been listed on the Frankfurt Stock Exchange since 2017 and is part of the MDAX stock market index.

Job Description
The Mar Tech analytics engineering team builds and operates the data foundation behind performance marketing at Delivery Hero: the production pipelines that cover ingestion, server-to-server activation, marketing feeds, measurement and acquisition optimisation, serving every brand in the group, including Glovo, Talabat and Pedidos Ya. We turn raw partner, app and order data into curated, trusted datasets that budget allocation, bidding, campaign activation and data science models depend on daily. Correctness here is not academic. When a pipeline is wrong, marketing spend moves in the wrong direction. We are looking for a Senior Data Engineer to own domains of this platform end-to-end: modelling, pipeline design, data quality, and the data contracts our stakeholders build on. You will work across a broad technical surface (Big Query, dbt, Airflow, Pub/Sub, Spark) at global scale, and you will be the person stakeholders come to when a domain needs to be designed, not just maintained. In this role you will, Own and evolve pipeline domains (ingestion, server-to-server activation, feeds, measurement curation) on Airflow, Big Query and dbt, from design and architecture through rollout and ongoing maintenance Take a domain from requirements to production, monitoring and daily operation, rather than handing it over at deployment Balance engineering rigour against business constraints and timelines Design the curated layer others build on Design and maintain the curated models that budget allocation, bidding and data science models consume, with clear contracts, documented semantics and stable interfaces Apply dimensional and incremental modelling, historisation, partitioning and clustering, and judge when each applies Keep a large transformation DAG maintainable as scope and team size grow Make reliability the default, not the reaction Define tests, freshness SLAs and anomaly checks across owned domains, reducing incident rate and time-to-detect instead of reacting after stakeholders notice Design for idempotency, deduplication, backfills and late-arriving data Improve runtime, cost and backfill behaviour of owned pipelines, with measurable before and after numbers Deliver activation and partner integrations Build reliable outbound flows to ad platforms (Adjust, Google, Smartly, Reddit, Pinterest, LINE, Channable, Braze), where correctness directly moves marketing spend Handle API, Pub/Sub and file-based ingestion (GCS, S3, Cloud SQL replicas) and the failure modes each brings Translate vague requests from data scientists, analysts and marketing teams into defined data contracts, escalating when blocked and pushing back on the wrong ones Reach up to staff and principal engineers on hard architectural questions rather than working in isolation Own lineage, access control, PII policy tags and GDPR-compliant handling across your domains Raise the engineering bar Review code, set standards, and mentor junior members of the chapter Drive knowledge sharing within the chapter and across Delivery Hero Qualifications We are looking for an engineer who has owned data domains in production rather than contributed to parts of them. Concretely: End-to-end pipeline ownership: taking a data domain from requirements through production, monitoring and daily maintenance. Advanced SQL and data modelling: deep Big Query skills, dimensional and incremental modelling, historisation, partitioning and clustering, and the judgment to know when each applies. dbt and transformation frameworks: model structure, tests, documentation and macros, keeping a large transformation DAG maintainable. Orchestration and integration patterns: Airflow DAG design, Pub/Sub, API and file-based ingestion (GCS, S3, Cloud SQL replicas), idempotency, deduplication, backfills and late-arriving data. Software engineering practice: strong Python and SQL with modularisation, testing, CI/CD, version control and monitoring. Data governance and privacy: lineage, access control, PII policy tags and GDPR-compliant handling in a multi-entity, multi-market environment. Stakeholder partnership under ambiguity: turning vague requests from data scientists, analysts and marketing teams into data contracts, escalating when blocked, and pushing back on the wrong request. AI fluency and agentic workflows: using LLM-driven or tool-using agents to scale data engineering work, with a clear view of where they help and where they do not. Technical maturity: knowing when to decide and when to reach up to staff and principal engineers, and distinguishing your own contribution from the team’s outcome. Mar Tech doma