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Machine Learning Astronomy Jobs in Georgia (NOW HIRING)

AMB Sports & Entertainment

Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... reporting, and machine learning initiatives * Build and optimize data models (dimensional ... Experience with data orchestration platforms such as Apache Airflow and/or Astronomer for pipeline ...

AEG

Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... reporting, and machine learning initiatives * Build and optimize data models (dimensional ... Experience with data orchestration platforms such as Apache Airflow and/or Astronomer for pipeline ...

All JobsMachine Learning Astronomy JobsMachine Learning Astronomy Jobs in Georgia

Machine Learning Astronomy information

What is the difference between Machine Learning Astronomy vs Data Scientist?

AspectMachine Learning AstronomyData Scientist
Required CredentialsDegree in Astronomy, Physics, or related fields; knowledge of machine learningDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch institutions, observatories, academiaCorporate, tech companies, consulting firms
Industry UsageAnalyzing astronomical data, developing models for celestial phenomenaBusiness analytics, predictive modeling, data visualization

Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.

What is machine learning astronomy?

Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.

What are the key skills and qualifications needed to thrive as a Machine Learning Astronomer, and why are they important?

To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.

What are some common challenges faced by professionals working in machine learning astronomy?

Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.
What are popular job titles related to Machine Learning Astronomy jobs in Georgia? For Machine Learning Astronomy jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in Georgia look for? The top searched job categories for Machine Learning Astronomy jobs in Georgia are:
What cities in Georgia are hiring for Machine Learning Astronomy jobs? Cities in Georgia with the most Machine Learning Astronomy job openings:
Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in Georgia as of June 2026, with employment types broken down into 84% Full Time, and 16% Part Time. Highlights an 100% In-person job distribution.
Data Engineer

Data Engineer

AMB Sports & Entertainment

Atlanta, GA • On-site

$110K - $132K/yr

Full-time

Posted 3 days ago

Job description

As a Data Engineer, you will own key parts of the pipeline lifecycle-from ingesting source data through transformation, testing, and publishing trusted datasets for downstream consumers. You'll partner closely with analysts and stakeholders to turn questions into durable data products, improve reliability and observability, and help standardize patterns that scale across teams. Success in this role looks like dependable pipelines, well-modeled data, and faster delivery of insights.
Responsibilities:
  • Design, develop, and maintain robust, scalable data pipelines and ETL/ELT workflows to support analytics, reporting, and machine learning initiatives
  • Build and optimize data models (dimensional, relational) across structured and semi-structured data sources including ticketing, fan engagement, broadcasting, and sponsorship data
  • Develop and maintain production-grade Python applications and scripts for data transformation, API integrations, and automation
  • Engineer solutions on Databricks or Snowflake for large-scale data processing, lakehouse architecture, and advanced analytics
  • Build and deploy serverless data solutions using Azure Functions for event-driven processing and microservice integrations
  • Design and implement data orchestration workflows using platforms such as Apache Airflow and/or Astronomer to ensure reliable, monitored, and scalable pipeline execution
  • Manage version control, CI/CD pipelines, and collaborative development workflows using Git-based platforms (GitHub, Azure DevOps)
  • Collaborate with data analysts, data scientists, and business stakeholders to translate requirements into technical solutions
  • Implement data quality frameworks, monitoring, and alerting to ensure data integrity and reliability across the platform
  • Contribute to the evolution of the data platform architecture, advocating for best practices in performance, security, and scalability
  • Participate in code reviews to uphold engineering standards

Qualifications:
  • Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field (or equivalent professional experience)
  • 3+ years of professional experience in data engineering or a related discipline
  • Strong relational database experience, including data modeling (star schema, snowflake schema, 3NF) and advanced SQL development (T-SQL, PL/SQL, or equivalent)
  • Proficiency in Python development for data engineering use cases (pandas, PySpark, API development, scripting, testing)
  • Hands-on experience with Databricks or Snowflake for data lakehouse/warehouse architecture and large-scale data processing
  • Experience building and deploying Azure Functions or similar serverless compute for data workflows
  • Working knowledge of Git-based platforms such as GitHub or Azure DevOps for version control, branching strategies, and CI/CD pipelines
  • Experience with data orchestration platforms such as Apache Airflow and/or Astronomer for pipeline scheduling, monitoring, and dependency management
  • Strong understanding of data warehousing concepts, ETL/ELT patterns, and data integration best practices
  • Excellent communication and collaboration skills with the ability to work cross-functionally in a fast-paced environment

Preferred Qualifications:
  • Industry certifications demonstrating proficiency in data engineering (e.g., Databricks Certified Data Engineer, Azure Data Engineer Associate DP-203, Snowflake SnowPro Core, Google Professional Data Engineer, AWS Data Engineer Associate)
  • Experience with a major cloud platform (Azure, AWS, or GCP) including infrastructure-as-code and cloud-native data services
  • Prior experience in sports, entertainment, media, or live events industries
  • Familiarity with streaming and real-time data technologies (Kafka, Event Hubs, Spark Structured Streaming)
  • Experience with data governance, cataloging, and lineage tools (Unity Catalog, Purview, Collibra)
  • Exposure to machine learning pipelines and MLOps practices
  • Experience with containerization (Docker, Kubernetes) and microservices architecture.

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