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 MarTech 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 PedidosYa.
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 (BigQuery, dbt, Airflow, Pub/Sub, Spark) at general scale, and you will be the person stakeholders come to when a domain needs to be designed, not just maintained.
In this role you willOwn data domains end-to-endOwn and evolve pipeline domains (ingestion, server-to-server activation, feeds, measurement curation) on Airflow, BigQuery and dbt, from design and architecture through rollout and ongoing maintenanceTake a domain from requirements to production, monitoring and daily operation,



rather than handing it over at deploymentBalance engineering rigour against business constraints and timelinesDesign the curated layer others build onDesign and maintain the curated models that budget allocation, bidding and data science models consume, with clear contracts, documented semantics and stable interfacesApply dimensional and incremental modelling, historisation, partitioning and clustering, and judge when each appliesKeep a large transformation DAG maintainable as scope and team size growMake reliability the default, not the reactionDefine tests, freshness SLAs and anomaly checks across owned domains, reducing incident rate and time-to-detect instead of reacting after stakeholders noticeDesign for idempotency, deduplication, backfills and late-arriving dataImprove runtime, cost and backfill behaviour of owned pipelines, with measurable before and after numbersDeliver activation and partner integrationsBuild reliable outbound flows to ad platforms (Adjust, Google, Smartly, Reddit, Pinterest, LINE, Channable, Braze), where correctness directly moves marketing spendHandle API, Pub/Sub and file-based ingestion (GCS, S3, CloudSQL replicas) and the failure modes each bringsTurn ambiguous requests into data contractsTranslate vague requests from data scientists, analysts and marketing teams into defined data contracts, escalating when blocked and pushing back on the wrong onesReach up to staff and principal engineers on hard architectural questions rather than working in isolationGovern data responsiblyOwn lineage, access control,



PII policy tags and GDPR-compliant handling across your domainsRaise the engineering barReview code, set standards, and mentor junior members of the chapterDrive knowledge sharing within the chapter and across Delivery HeroQualificationsEnd-to-end pipeline ownership: taking a data domain from requirements through production, monitoring and daily maintenance.
Advanced SQL and data modelling: deep BigQuery 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, CloudSQL 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.
MarTech domain exposure: attribution, ad platform APIs, server-to-server conversion flows,

📌 Senior Data Engineer – MarTech (Barcelona)
🏢 Glovo Group
📍 Barcelona