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.

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

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

Responsibilities
Existing platform (Databricks)

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- 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)

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- 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

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

Technical Requirements
Databricks

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- PySpark and SQL — able to read, debug, and modify existing pipelines. Deep Spark tuning is not required.

- Unity Catalog: catalogs, schemas, grants, lineage, and the metastore model.

Snowflake

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- Warehouses, roles and grants, and the general operating model.





- Query performance and an awareness of how compute cost behaves.

Dbt

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- Models, sources, tests, and incremental materializations.

- Project structure and how dbt fits into a deployment workflow.

Airflow

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- Writing and maintaining DAGs, operators, scheduling, and dependency management.

- Understanding retries, backfills, and idempotent task design.

- 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

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- 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

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- Experience with an vigente 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. xhfqzwm

What We Offer

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- Commitment to Professional Development

- Flexible and Collaborative Culture

- Total Rewards

- Specific benefits are determined by the employment type and location.

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📌 Data Engineer (Databricks ) (Barcelona)
🏢 Wizeline
📍 Barcelona