Data Engineer - Pipelines, ETL & ML Collaboration

Onyx Premier Inc.; Albuquerque (NM); On-site; USD 100,000 - 136,000; Full time

Job summary

Onyx Premier Inc. in Albuquerque, NM is seeking a Data Engineer to design, build, and maintain robust data pipelines to support analytics and machine learning initiatives.

You will collect, process, and store large data from diverse sources, ensure quality and integrity, and optimize workflows for performance and cost. Collaborate with data science and software teams to ensure scalable data infrastructure, and contribute to ETL, data warehousing, and modeling efforts.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, or a related field.
• 3-5 years of experience in data engineering or a similar role.
• Proficiency in Python, SQL, and at least one programming language such as Java or Scala.
• Experience with big data technologies like Hadoop, Spark, or Kafka.
• Familiarity with cloud platforms (AWS, Azure, GCP).

Responsibilities

• Design and implement data pipelines and ETL processes to extract, transform, and load data from various sources.
• Develop and maintain data warehouses and databases using SQL and NoSQL technologies.
• Optimize data storage and processing to improve performance and reduce costs.
• Collaborate with data scientists and software engineers to integrate data pipelines with ML models.
• Monitor and troubleshoot data pipeline issues to ensure data quality and availability.
• Stay up-to-date with emerging data engineering technologies and best practices.

Skills

Python

Java

Scala

SQL

Data pipelines

Education

Bachelor's degree in Computer Science or related field

Tools

Apache Airflow

Kafka

Spark

Hadoop

AWS / Azure / GCP

Job description

Onyx Premier Inc. in Albuquerque, NM is seeking a Data Engineer to design, build, and maintain robust data pipelines to support analytics and machine learning initiatives.

You will collect, process, and store large data from diverse sources, ensure quality and integrity, and optimize workflows for performance and cost. Collaborate with data science and software teams to ensure scalable data infrastructure, and contribute to ETL, data warehousing, and modeling efforts.