Location: Madrid, Spain – Hybrid Work Hours: Swiss/Spanish business hoursExperience: 6–10+ yearsEngagement: Contract ( 6 months with extensions)Start: Immediate / ASAP The Opportunity We are looking for a highly hands‑on Senior Databricks Data Engineer to design, build, and optimize scalable end‑to‑end data pipelines. This role is best suited to an engineer who is comfortable writing Py Spark/Python code independently, working with large datasets, integrating multiple data sources, and taking data from initial ingestion through Bronze, Silver, and Gold layers using Medallion Architecture. This is not a coordination‑only or architecture‑only position. We are specifically seeking someone who remains hands‑on with Databricks, Spark, Py Spark, Python and SQL. Key Responsibilities Design, develop, and maintain scalable Databricks‑based data pipelines. Build end‑to‑end data engineering solutions from source ingestion through Gold‑layer datasets. Develop high‑performance data processing workflows using Py Spark, Python, Spark and SQL. Integrate data from multiple sources, including APIs, databases, files, cloud storage and external platforms. Design and optimize Databricks architectures for data ingestion, transformation, processing and storage. Work with large‑scale datasets and distributed data processing environments. Perform Spark/Databricks performance tuning to improve processing speed, scalability and cost efficiency. Build robust ETL/ELT workflows with appropriate data quality, monitoring and governance controls. Optimize Databricks workflows,



jobs and compute resources. Troubleshoot pipeline performance, reliability and data‑quality issues. Collaborate with Data Architects, Data Engineers and business stakeholders to translate requirements into production‑ready data solutions. Contribute to engineering standards and Databricks best practices. Mandatory Technical Skills you demonstrate strong production‑level experience with: Databricks Python Advanced SQL Medallion Architecture – Bronze, Silver and Gold Data ingestion from APIs, databases, files and multiple source systems Large‑scale/distributed data processing Data transformation and aggregation Databricks/Spark performance tuning and optimization Data quality and pipeline monitoring Cloud‑based data engineering environments Highly Desirable Experience with some of the following would be advantageous: AWS: S3, Glue ETL, Lambda, Step Functions, ECS, Cloud Watch Snowflake DBT Terraform Big Query Kafka Git / Jenkins / CI/CD Data governance Cost monitoring and cloud optimization Infrastructure as Code Databricks Certified Data Engineer Associate or similar Databricks certification is considered an asset.



Adecuado Candidate You are a strong fit if you have personally designed and coded production data pipelines rather than primarily managing other engineers. You should be able to clearly explain a recent project where you: and describe the Py Spark/Python code, transformations, architecture decisions, performance improvements and data‑quality controls you personally implemented. Technical Screening Questions Shortlisted candidates should be prepared to discuss: 1. Databricks / Medallion Architecture: Walk us through a production pipeline you personally built from source ingestion through Bronze, Silver and Gold. What did you personally code? 2. Py Spark: Describe a Py Spark pipeline you developed for a large dataset. What transformations did you implement, and how did you optimize its performance? 3. Performance: A Databricks/Spark job that previously completed in 20 minutes now takes 90 minutes. How would you diagnose and optimize it? 4. Data Ingestion: How have you ingested data from APIs, relational databases, files or cloud storage into Databricks? 5. Data Quality: How do you implement data‑quality validation, error handling, monitoring and recovery within a production data pipeline? 6. Optimization: Give an example where you reduced Databricks/cloud processing costs or significantly improved pipeline performance. Important: We are prioritizing hands‑on engineers, not candidates whose recent experience is primarily management, coordination or high‑level architecture. #J-18808-Ljbffr

📌 Databricks Data Engineer (Madrid)
🏢 Talent To Hire
📍 Madrid