Senior Data Engineer – MarTech

Role Overview

The MarTech analytics engineering team builds and operates the data foundation behind performance marketing at Delivery Hero. The team turns raw partner, app, and order data into curated, trusted datasets that budget allocation, bidding, campaign activation, and data science models depend on daily.

What You Will Do

As a Senior Data Engineer, you will own data domains end-to-end, design and maintain the curated layer others build on, and balance engineering rigour against business constraints and timelines.

Why It Might Be a Fit

We are looking for an engineer who has owned data domains in production rather than contributed to parts of them. 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.

Requirements

• 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.
• 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.

Benefits

• Top-notch private health insurance
• Monthly Glovo credit
• Cobee discounts on transportation, food, and even kindergarten expenses
• Discounted gym memberships
• Extra time off
• The freedom to work from home two days a week
• The opportunity to work from anywhere for up to three weeks a year
• Enhanced parental leave
• Office-based nursery
• Online therapy and wellbeing benefits