# Mid Data Engineer (Barcelona hybrid) at WIZELINE

> Spain · Mid-level · Data engineering · open. Source: https://alderhq.app/jobs/greenhouse%3Awizeline%3A7475540 (read 2026-11-08).

## Facts (observed)

- Company: [WIZELINE](https://alderhq.app/companies/wizeline)
- Location text: Barcelona; Spain
- Where it can be done from (inferred): Spain (evidence: "Location: Barcelona; Spain")
- Posted: 2025-12-16 (327 days ago)
- First seen by alder: 2026-11-07 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://www.wizeline.ai/careers/job?gh_jid=7475540
- Skills listed (inferred): Python, AWS, Spark, Airflow, Snowflake, dbt, SQL

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Spain, n 6): p25 €63k, median €70k, p75 €70k

## Description (the employer's text)

### We are

Wizeline, a global AI-native technology solutions provider, develops cutting-edge, AI-powered digital products and platforms. We partner with clients to leverage data and AI, accelerating market entry and driving business transformation. As a global community of innovators, we foster a culture of growth, collaboration, and impact.

With the right people and the right ideas, there’s no limit to what we can achieve

### Are you a fit?

Sounds awesome, right? Now, let’s make sure you’re a good fit for the role:

### Responsibilities

### Existing platform (Databricks)

- Keep production pipelines running: ingestion, transformation, and delivery to downstream consumers.
- Diagnose and resolve pipeline failures and data quality issues, often without documentation to fall back on.
- Reverse-engineer and document existing transformation logic and business rules — this is the input the migration depends on.
- Migrate legacy tables from Hive Metastore to Unity Catalog.
- Maintain Iceberg-enabled table sharing between Databricks and Snowflake.
### New development (Snowflake, dbt, Airflow)

- Build and test dbt models, including incremental materializations and data tests.
- Develop and maintain Airflow DAGs for orchestration.
- Validate that migrated pipelines produce output equivalent to the Databricks versions.
- Contribute to Snowflake modeling, performance, and cost decisions.
### Across both

- Work directly with client stakeholders on technical topics, alongside the team lead.
### Technical Requirements

### Databricks

- PySpark and SQL — able to read, debug, and modify existing pipelines. Deep Spark tuning is not required.
- Delta Lake: MERGE/upsert patterns, table properties, OPTIMIZE, partitioning.
- Databricks Workflows, cluster configuration, job troubleshooting.
- Unity Catalog: catalogs, schemas, grants, lineage, and the metastore model.
### Snowflake

- Warehouses, roles and grants, and the general operating model.
- Query performance and an awareness of how compute cost behaves.
### Dbt

- Models, sources, tests, and incremental materializations.
- Project structure and how dbt fits into a deployment workflow.
### Airflow

- Writing and maintaining DAGs, operators, scheduling, and dependency management.
- Understanding retries, backfills, and idempotent task design.
### Fundamentals

- 3+ years operating production data pipelines.
- Strong SQL — window functions, complex joins, reading transformation logic written by someone else.
- Python for scripting, automation, and API integration.
- Incremental loading patterns, idempotency, late-arriving data, reprocessing.
- AWS: S3, IAM basics. Basic working knowledge of Redshift and its role in the wider architecture.
### Ways of working

- Fluent English — client-facing role with stakeholders based abroad.
- Self-directed. Able to make progress on an unfamiliar codebase without a structured onboarding path, and comfortable asking good questions when context is missing.
- Clear communicator: can explain a production incident to a non-technical stakeholder and give a realistic ETA.
### Nice-to-have

- Experience with an actual platform migration, not only greenfield work.
- Open table formats, particularly Iceberg and cross-platform sharing.
- Clickstream or web analytics data (Adobe Analytics, Google Analytics, Segment).
- Experience taking over an undocumented system and stabilizing it.
- AI Tooling Proficiency: Leverage one or more AI tools to optimize and augment day-to-day work, including drafting, analysis, research, or process automation. Provide recommendations on effective AI use and identify opportunities to streamline workflows.
### What we offer

- A High-Impact Environment
- Commitment to Professional Development
- Flexible and Collaborative Culture
- Global Opportunities
- Vibrant Community
- Total Rewards
\*Specific benefits are determined by the employment type and location.

Find out more about our culture here.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Awizeline%3A7475540 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.