Location:

Madrid, Spain – Hybrid Work Hours:

Swiss/Spanish business hours Experience:

6–10+ years Engagement:

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

PySpark/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, PySpark, 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

PySpark, 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, CloudWatch Snowflake DBT Terraform BigQuery 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. Idóneo 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

PySpark/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. PySpark: Describe a PySpark 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.

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