Data Engineer

Matrix Global

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

Posted 9/28/2026full-timeRemoteJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and maintaining production-grade data pipelines, with strong capabilities in data transformation, machine learning feature engineering, and cross-functional collaboration. Proficient in utilizing tools such as Apache Spark, SQL, and Python to optimize large-scale data architectures and enhance system performance.

Highest-signal resume keywords

Apache SparkSQLData Pipeline OptimizationMachine Learning Feature EngineeringCross-Functional Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Apache SparkSQLPythonData TransformationMachine LearningHadoop EcosystemVersion Control (Git)Data CleansingFeature EngineeringElasticsearch

Soft Skills

Analytical SkillsCustomer-Facing SkillsCollaboration Skills

Tools & Technologies

Apache HadoopHiveImpalaHDFSSqoopZeppelinJupyterJenkinsApache AirflowDocker

Industry Keywords

Data ScienceData EngineeringMachine LearningData AnalyticsData Pipelines

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 closely 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 and Spanish, both written and spoken
Experience working with Linux
Experience building machine learning pipelines
Knowledge of Elasticsearch
Experience with Zeppelin and/or Jupyter
Familiarity with workflow orchestration tools such as Jenkins or Apache Airflow
Experience with microservices and containerization technologies, including Docker and Kubernetes

Benefits

Comp & perks

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