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