Senior Data Engineer – MarTech

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

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 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.