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# Data Engineer

- **Company:** Latam. Titan Os
- **Location:** Barcelona, Spain (Remote available)
- **Salary:** €70,000.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Airflow, Amazon Web Services, Amazon S3, Automation of Tests, Microsoft Azure, Cloud Computing, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Mining, Data Systems, DevOps, Python (Programming Language), DataOps, SQL Databases, Google Cloud, Data Ingestion, Snowflake, Apache Spark, Infrastructure as Code (IaC), Data Management, Software Version Control, Amazon Redshift, Databricks
- **Published:** September 5, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ Solid proven experience in a data engineering role
 + Strong programming skills in Python
 + Proficiency in SQL and data modeling techniques
 + Knowledge of data extraction and ETL frameworks, ETL pipeline orchestration (i.e. Airflow) and ETL cloud infrastructure (preferably on AWS)
 + Experience with cloud platforms (AWS, Azure, Google Cloud) and associated services like S3, Redshift, Databricks, or Snowflake
 + A DevOps mindset; used to having end-to-end responsibility in a project
 + Excellent communication and collaboration skills
 + Fluent in English
 + Experience with high-volume telemetry or clickstream data
Nice to have:

 + Familiarity with API integration and data extraction methods
 + Experience with SQLMesh for data transformation and modeling
 + Expertise with Spark and Scala
 + Experience working with Agile methodologies
 + Understanding of data governance best practices

## Description

+ Design and implement the integration of additional data solutions and pipelines
 + Develop and maintain AWS infrastructure related to data ingestion and processing
 + Optimize data management and processing, both in terms of budget and performance
 + Work with stakeholders to identify root causes of inaccurate data points and develop fixes
 + Design and implement Data Observability frameworks to monitor data health, schema drift, and pipeline performance, ensuring high reliability before data reaches downstream stakeholders.
 + Standardize DataOps practices, including version control for data models, automated testing, and Infrastructure as Code (IaC) to streamline deployments.


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