Mid Data Engineer (Barcelona hybrid)

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 .