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Delivery Hero (talabat)

Senior Data Engineer – MarTech

Delivery Hero (talabat) is hiring a Senior Data Engineer – MarTech in Barcelona, Spain. Level rates it ; you can apply on Delivery Hero (talabat).

Level's read on this job

  • Level rates this data engineer role Little AI ●○○○: aI is not part of the work.
  • Tools and skills the posting cites include AI agents.
  • Level classifies it as a senior role.
  • Level lists 896 other open roles at Delivery Hero (talabat).
  • The role was posted within the past week.

AI in this role

ai-agents

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.

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 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 data domains end-to-end

  • Own 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 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, CloudSQL replicas) and the failure modes each brings

Turn ambiguous requests into data contracts

  • 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

Govern data responsibly

  • 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

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 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, feeds and audience syncs. A plus, not a requirement.

Individuals representing diverse profiles, encompassing various genders, ethnicities, and backgrounds, are less likely to apply for this role if they do not possess solid experience in 100% of these areas. Even if it seems you don't meet our musts don't let it stop you, we are all about finding the best talent out there! Skills can be learned, and embracing diversity is invaluable.

We believe driven talent deserves:

  • 💪 Top-notch private health insurance to keep you at your peak.

  • 🍔 Monthly Glovo credit to satisfy your cravings!

  • 💳 Cobee discounts on transportation, food, and even kindergarten expenses.

  • 🏊 Discounted gym memberships to keep you energized.

  • 🏖️ Extra time off, the freedom to work from home two days a week, and the opportunity to work from anywhere for up to three weeks a year!

  • 👪 Enhanced parental leave, and office-based nursery.

  • 🧠 Online therapy and wellbeing benefits to ensure your mental well-being.

Delve into our culture by taking a peek at our Instagram and check out our Linkedin and website!

Additional information

At Glovo, your success is defined by both results and behaviors. We hire for excellence in craft and for the Leadership Principles that shape how we think, decide, and collaborate. What you achieve matters and how you achieve it defines us. Together, they move the business forward and deliver great experiences. Learn more about our Leadership Principles here.

Here at Glovo, we thrive on diversity, we believe it enhances our teams, products, and culture. We know that the best ideas come from a mashup of brilliant diverse minds. This is why we are committed to providing equal opportunities to talent from all backgrounds – all genders, racial/diverse backgrounds, abilities, ages, sexual orientations and all other unique characteristics that make you YOU. We will encourage you to bring your authentic self to work, fostering an inclusive environment where everyone feels heard.

Feel free to note your pronouns in your application (e.g., she/her/hers, he/him/his, they/them/theirs, etc).

So, ready to take the wheel and make this the ride of your life?

Delve into our culture by taking a peek at our Instagram and check out our LinkedIn and website!

How we rate this

Senior Data Engineer – MarTech at Delivery Hero (talabat) rates 25 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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