Data Engineer (Databricks )

Wizeline; Barcelona; Presencial; EUR 65.000 - 90.000; Jornada completa

Descripción de la vacante

Wizeline in Barcelona seeks a data platform engineer to migrate and optimize pipelines across Databricks and Snowflake. You will build dbt models, manage Airflow orchestration, and ensure output parity between platforms while controlling costs.

You’ll work with client stakeholders, apply SQL and Python to transform data, and contribute to architecture decisions in a fast-growing AI-enabled environment.

Formación

• 3+ years operating production data pipelines.
• Strong SQL</br>— window functions, complex joins.
• Python for scripting, automation, and API integration.
• Experience with Databricks, Snowflake, and dbt workflows.
• Familiar with AWS basics (S3, IAM).
• Knowledge of Iceberg and cross-platform sharing is a plus.

Responsabilidades

• Keep production pipelines running: ingestion, transformation, and delivery to downstream consumers.
• Diagnose and resolve pipeline failures and data quality issues.
• Migrate legacy tables and maintain cross-platform sharing between Databricks and Snowflake.
• Build and test dbt models and incremental materializations.
• Develop and maintain Airflow DAGs for orchestration.
• Validate migrated pipelines produce output equivalent to Databricks versions.
• Work with client stakeholders and team lead on technical topics.

Conocimientos

Databricks

Snowflake

dbt

Airflow

Python

SQL

Unity Catalog

AWS

Herramientas

PySpark

Redshift

Iceberg

Descripción del empleo

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

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.
• 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.
• 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

• Commitment to Professional Development
• Flexible and Collaborative Culture
• Total Rewards
• Specific benefits are determined by the employment type and location.