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?onSenior Databricks Data Engineerto design, build, and optimize scalable end?to?end data pipelines.

This role is best suited to an engineer who is comfortable writingPySpark/Python code independently, working with large datasets, integrating multiple data sources, and taking data from initial ingestion throughBronze, Silver, and Gold layers using Medallion Architecture.

This is not a coordination?only or architecture?only position.

We are specifically seeking someone who remainshands?on with Databricks, Spark, PySpark, Python and SQL.

Key Responsibilities

Design, develop, and maintain scalableDatabricks?based data pipelines.

Build end?to?end data engineering solutions fromsource ingestion through Gold?layer datasets.

Develop high?performance data processing workflows usingPySpark, Python, Spark and SQL.

Integrate data from multiple sources, includingAPIs, databases, files, cloud storage and external platforms.

Design and optimize Databricks architectures fordata ingestion, transformation, processing and storage.

Work withlarge?scale datasets and distributed data processingenvironments.

PerformSpark/Databricks performance tuningto improve processing speed, scalability and cost efficiency.

Build robust ETL/ELT workflows with appropriatedata 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 andDatabricks best practices.

Mandatory Technical Skills

you demonstrate strong production?level experience with:

Databricks

Python

Advanced SQL

Medallion Architecture – Bronze, Silver and Gold

Data ingestion fromAPIs, databases, files and multiple source systems

Large?scale/distributed data processing

Data transformation and aggregation

Databricks/Sparkperformance 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 Associateor 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 thePySpark/Python code, transformations, architecture decisions, performance improvements and data?quality controlsyou 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 prioritizinghands?on engineers, not candidates whose recent experience is primarily management, coordination or high?level architecture.

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📌 Data Engineer (Snowflake, AWS), hibrido (Madrid)
🏢 Talent To Hire
📍 Madrid