Data Operations Engineer III at Zone 5 Technologies
San Luis Obispo, California, USA -
Full Time


Start Date

Immediate

Expiry Date

28 Nov, 25

Salary

170000.0

Posted On

28 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Defense, Data Engineering, Data Validation, Professional Development, Computer Science, Aerospace

Industry

Information Technology/IT

Description

Join us at Zone 5 Technologies where innovation and cutting-edge technology drive pioneering advancements in unmanned aircraft systems (UAS). Based in the vibrant city of San Luis Obispo CA, we’re seeking talented individuals to join and contribute to our growing team.
We thrive on pushing boundaries and inventing solutions to real-world challenges. Join a community of forward-thinkers who collaborate to create impactful products with a global reach. Our team, consisting of engineers and professionals, is prepared to overcome new frontiers and deliver innovative solutions in the field of unmanned aircraft systems.
Join our dynamic team as a Data Operations Engineer III to design and maintain robust, scalable data pipelines that convert terabytes of unstructured field data into clean, versioned datasets for model training and analysis.

QUALIFICATIONS:

  • Bachelor’s degree in Computer Science, Data Engineering, or a related technical field (or equivalent experience)
  • 5-7+ Strong experience in Python and data management tools
  • Familiarity with at least one distributed processing framework
  • Experience with data validation, schema enforcement, and QA tooling
  • Understanding of basic statistics and data profiling

How To Apply:

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Responsibilities

Ingestion & Streaming

  • Design and implement data ingestion pipelines for AI/ML purposes based on efficient and industry standard practices
  • Establish field methods to capture metadata and data from the field
  • Enforce schema validation and integrity checks on data ingestion

ETL & Dataset Versioning

  • Build and manage DAG pipelines to generate datasets
  • Track and manage data versioning and lineage

Quality & Governance

  • Integrate tools like for validation and data profiling
  • Coordinate outsourced data labeling workflows and QA sampling plans
  • Build internal dashboards for visibility into data quality and flow health
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