This position is at Adevinta España

The selection process will be entirely managed by Adevinta España.

--

As a Data Engineer (Analytics & Products) at Kleinanzeigen, you start where the data need begins — not where the code does. You will work directly with product managers, analysts, and domain stakeholders to understand what decisions the data needs to support, how it will be consumed, and what already exists in the platform before writing a single line. From there, you design and build data assets that are homogeneous with the existing stack, cost-effective at scale, maintainable by the next engineer, and self-discoverable, so consumers can find, understand, and trust what you built without asking you. You will own the full lifecycle: ingestion through Airflow DAGs, transformation across dbt stage, core, and report layers on Databricks, and the quality, documentation, and observability that make those assets production-grade. You stay current with Airflow and design DAG topology deliberately, knowing when to use dynamic task mapping, data-aware scheduling, the TaskFlow API, or a Cosmos dbt task group with Watcher mode for performance-critical pipelines. You understand Spark well enough to know why a dbt incremental model running in Databricks produces a full table scan instead of a partition filter push-down, how a poorly configured merge operation compounds into file fragmentation over time, and what to do about it, whether that means adjusting the incremental strategy, adding liquid clustering, running OPTIMIZE, or rethinking the model grain entirely. You write Python and shell scripts as naturally as SQL and follow engineering core principles: modularity, idempotency, testability, not because they are rules, but because they make your work last.



You operate in an AI-First engineering model: AI handles execution; you own intent, precision, and correctness. You use it to automate, to accelerate best practices, and to raise the quality bar across the team, not to ship faster with less judgment. You will collaborate closely with analysts, product managers, data platform engineers, and stakeholders across the company to ensure that the datasets we create and maintain in our data platform are reliable, trustworthy, and built to serve the decisions that matter.

What We're Looking For

Required

- Advanced SQL: window functions, CTEs, query optimisation, partitioning and clustering, with enough depth to understand execution plans and diagnose performance issues
- Hands-on production experience with dbt across the medallion architecture: models, tests, unit tests, macros, snapshots, YAML documentation and materialisation strategies
- Experience building and operating Airflow DAGs using Python operators, with the ability to design DAG topology deliberately for different pipeline requirements
- Proficiency in Python and shell scripting for pipeline development, ingestion tasks and operational tooling
- Solid understanding of lakehouse concepts and data formats: Delta Lake for ACID-compliant storage, Parquet for columnar efficiency, and Avro for event-driven schema definition




- Ability to work independently: owning tickets from design through delivery, including incident response and backfills
- Ability to gather requirements from non-technical stakeholders, understand consumption patterns, and translate business needs into data models that are accurate, maintainable and cost-effective
- Experience defining and maintaining schema definitions (Avro/ODIN or equivalent schema registry)
- Fluency with AI-assisted development workflows as part of daily practice: authoring intent specifications, verifying and owning AI-generated outputs, and contributing to shared context and tooling so the whole team benefits
- Experience with semantic layer concepts: defining reusable metrics, dimensions, and business logic that serve as a single source of truth across consumers (e.g. dbt Semantic Layer, Looker LookML, or equivalent)
- Spark comprehension at the level needed to understand how Databricks processes dbt models, read execution plans, and diagnose and fix performance issues such as full table scans, merge inefficiencies and file fragmentation
- Engineering fundamentals applied to data work: modularity, idempotency, testability and single responsibility as default practices, not afterthoughts
- Experience treating cost as a first-class constraint: identifying and acting on optimisation opportunities across compute, storage and pipeline design

Nice to Have
- Experience with Kafka or event-driven ingestion
- Familiarity with Salesforce, Google Ads, Meta, or other marketing/CRM APIs
- Experience with data modelling patterns and when to apply each: dimensional modelling, star schema, wide tables, one big table, slowly c

📌 Data Engineer (España)
🏢 Adevinta España
📍 España