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.