Data Engineer for Real-Time Farm Data Platforms

Speria

Atlanta (GA)

On-site

USD 95,000 - 150,000

Full time

14 days+
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Job summary

Speria MTech is hiring a Data Engineer to join our Atlanta team. You will design, build, and maintain scalable data pipelines and platforms supporting machine learning, optimization, and operational reporting systems.

The role focuses on transforming data from sensors, controllers, and enterprise systems into high-quality datasets using batch and scheduled processing workflows, with support for evolving near real-time use cases. We value a service-minded attitude and collaboration.

Qualifications

  • Bachelor’s degree in Computer Science, Engineering, or related field.
  • 2–4 years experience in data engineering or data systems.
  • Experience with Apache Spark and large-scale data processing.
  • Experience building batch pipelines in production.
  • Experience with cloud platforms (e.g., Azure, Databricks).
  • Strong Python programming skills.
  • SQL and Apache Spark expertise.

Responsibilities

  • Design and build batch and near real-time data pipelines.
  • Develop transformation workflows for high-quality datasets.
  • Ensure data quality, integrity, and reliability.
  • Collaborate with MLEs for feature data readiness.
  • Implement data monitoring and anomaly detection.
  • Support synthetic data for testing and simulation.
  • Optimize pipelines to remove redundancy and reduce costs.
  • Improve performance, scalability, and efficiency.
  • Maintain pipeline reliability and documentation.

Skills

Apache Spark
Batch pipelines
Azure
Databricks
Python
SQL

Education

Bachelor’s degree in Computer Science, Engineering, or related field

Job description

Speria MTech is hiring a Data Engineer to join our Atlanta team. You will design, build, and maintain scalable data pipelines and platforms supporting machine learning, optimization, and operational reporting systems.

The role focuses on transforming data from sensors, controllers, and enterprise systems into high-quality datasets using batch and scheduled processing workflows, with support for evolving near real-time use cases. We value a service-minded attitude and collaboration.

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