Senior Data Engineer – MarTech

Glovo; Vilanova y Geltrú; On-site; EUR 70,000 - 110,000; Full time

Job summary

Glovo Group, part of Delivery Hero, seeks a Senior Data Engineer to own MarTech data domains end-to-end, shaping pipelines, data models and contracts that power marketing across brands.

You will work with BigQuery, dbt, Airflow and Spark at global scale, ensure reliability and governance, translate ambiguous requests into concrete data contracts, and mentor teammates.

Qualifications

• 5+ years of data engineering experience in a data-driven environment.
• Experience designing and operating data pipelines and data contracts.

Responsibilities

• Own data domains end-to-end from design to production and maintenance.
• Design curated models and enforce clear contracts and interfaces.

Skills

Cross-functional collaboration

Problem solving

Communication

Mentoring

Tools

BigQuery

dbt

Airflow

Spark

Pub/Sub

GCS

S3

CloudSQL

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 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