Data Engineer

Matrix Global

. Develop, implement, and maintain production-grade data pipelines based on data science requirements .

Posted 10/14/2026full-timeSpainJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and maintaining production-grade data pipelines, with a strong focus on machine learning and data transformation. Proficient in collaborating with cross-functional teams to enhance system performance and customer engagement.

Highest-signal resume keywords

Apache SparkSQLData TransformationMachine Learning Feature EngineeringPython

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Apache SparkSQLData TransformationMachine Learning Feature EngineeringPythonPySparkScalaJavaRVersion Control (Git)

Soft Skills

Analytical SkillsCustomer-Facing SkillsCross-Functional Collaboration

Tools & Technologies

Apache HadoopHiveImpalaHDFSSqoopLinuxElasticsearchZeppelinJupyterJenkins

Certifications & Qualifications

Bachelor's Degree in Computer ScienceStatisticsInformaticsInformation SystemsEngineering

Industry Keywords

Data PipelinesMachine LearningData ScienceData AnalyticsData Flows

Tech Stack

Tools & technologies

AirflowApacheDockerElasticSearchHadoopHDFSJavaJenkinsKubernetesLinuxMicroservicesPandasPySparkPythonScalaSparkSQL

About the role

Key responsibilities & impact

Develop, implement, and maintain production-grade data pipelines based on data science requirements
Design and build scalable data flows for specific business use cases that can be integrated into the product
Build and maintain machine learning data pipelines
Create tools and frameworks that support data scientists and analytics teams in developing and optimizing innovative solutions
Collaborate with Product, R&D, Data, and Analytics teams to enhance system functionality and performance
Train customer data scientists and engineers on maintaining and enhancing data pipelines within the platform
Travel domestically and internationally to support customers when required
Build and manage strong technical relationships with customers and partners

Requirements

What you’ll need

2+ years of hands-on experience with Apache Spark (required)
Strong experience with SQL
Experience using version control systems, particularly Git
Hands-on experience with the Apache Hadoop ecosystem, including Hive, Impala, Hue, HDFS, and Sqoop
Experience with Python (Pandas)
Experience with PySpark, Scala, Java, or R
Proven experience in data transformation, validation, cleansing, and machine learning feature engineering
Bachelor's degree or higher in Computer Science, Statistics, Informatics, Information Systems, Engineering, or another quantitative field
Experience working with and optimizing large-scale data pipelines, architectures, and datasets
Strong analytical skills and experience working with structured and semi-structured data
Experience building processes that support data transformation, metadata management, dependency management, and workload optimization
Ability to perform root cause analysis on data and processes to answer business questions and identify improvement opportunities
Strong customer-facing and cross-functional collaboration skills
Fluent in English preferred but not mandatory; Spanish
Experience with Linux is nice to have
Experience building machine learning pipelines is nice to have
Knowledge of Elasticsearch is nice to have
Experience with Zeppelin and/or Jupyter is nice to have
Familiarity with workflow orchestration tools such as Jenkins or Apache Airflow is nice to have
Experience with microservices and containerization technologies, including Docker and Kubernetes, is nice to have

Benefits

Comp & perks

Competitive compensation and benefits
Meaningful career development opportunities
Diverse, inclusive culture
Continuous learning and growth
Challenging and engaging work environment
Collaboration opportunities with a talented global team