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Machine Learning Data Engineer Jobs in Atlanta, GA

Cox Automotive

Machine Learning Lead Engineer

Redan, GA · On-site

$134K - $224K/yr

Analyzes complex data sets to solve real-world business and customer use cases. * Performs end-to-end development of machine learning models * May assist with or lead the development of industry ...

Cox Automotive

Machine Learning Lead Engineer

Marietta, GA · On-site

$134K - $224K/yr

Analyzes complex data sets to solve real-world business and customer use cases. * Performs end-to-end development of machine learning models * May assist with or lead the development of industry ...

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All JobsMachine Learning Data Engineer JobsMachine Learning Data Engineer Jobs in Atlanta, GA

Machine Learning Data Engineer information

See Atlanta, GA salary details

$42.8K

$124.7K

$170.7K

How much do machine learning data engineer jobs pay per year?

As of Jul 10, 2026, the average yearly pay for machine learning data engineer in Atlanta, GA is $124,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,100.00 and $132,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Data Engineer position, and why are they important?

To thrive as a Machine Learning Data Engineer, you typically need strong programming skills in Python or Scala, a deep understanding of data structures, algorithms, and machine learning concepts, as well as a degree in computer science or a related field. Experience with big data tools like Spark, Hadoop, and cloud platforms such as AWS or Azure, along with knowledge of data pipelines and ETL processes, is highly valuable; certifications in these areas can be advantageous. Problem-solving ability, attention to detail, and strong communication skills help professionals excel when working with diverse technical teams and stakeholders. These skills ensure data engineers can effectively build reliable, scalable data systems that support the development and deployment of machine learning models.

Can a data engineer become a machine learning engineer?

A data engineer can transition to a machine learning engineer role by gaining knowledge of machine learning algorithms, model development, and deployment techniques. Skills in programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and understanding of data pipelines are essential for this progression.

What is a Machine Learning Data Engineer job?

A Machine Learning Data Engineer is responsible for designing, building, and maintaining the data infrastructure that supports machine learning models. They develop data pipelines, ensure data quality, and optimize data storage for efficient processing. This role involves working with large-scale datasets, implementing ETL processes, and collaborating with data scientists to deploy machine learning models. Strong knowledge of databases, cloud platforms, and programming languages like Python and SQL is essential. Their work enables organizations to leverage machine learning effectively by providing reliable and scalable data solutions.

Will MLE be replaced by AI?

Machine Learning Data Engineers (MLEs) design, build, and maintain data pipelines and models that AI systems rely on. While AI automation tools can handle some tasks, MLE skills in data engineering, programming, and system architecture remain essential for developing and managing AI infrastructure effectively. The role is expected to evolve with advancements in AI, but it is unlikely to be fully replaced in the near future.

What are the typical daily responsibilities of a Machine Learning Data Engineer?

As a Machine Learning Data Engineer, your daily responsibilities often include designing, building, and maintaining data pipelines that efficiently move and transform data for machine learning applications. You may clean, preprocess, and validate large datasets, optimize storage solutions, and work closely with data scientists to ensure data is accessible and usable for model training and evaluation. Regular collaboration with software engineers and business analysts is common to align project goals and solve data-related challenges. Staying up to date with the latest tools and technologies is also important, as you'll help enable scalable and efficient deployment of machine learning solutions.

What engineers make $500,000?

Senior machine learning data engineers with extensive experience, advanced skills in data architecture, and proficiency in tools like Spark and cloud platforms can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership responsibilities, and industry reputation.

What is a $900000 AI job?

A $900,000 AI-related job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data modeling, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees. Compensation at this level reflects the value of expertise in developing and deploying complex AI systems in competitive industries.
What are popular job titles related to Machine Learning Data Engineer jobs in Atlanta, GA? For Machine Learning Data Engineer jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Data Engineer jobs in Atlanta, GA look for? The top searched job categories for Machine Learning Data Engineer jobs in Atlanta, GA are:
Machine Learning Data Engineer jobs near you
Infographic showing various Machine Learning Data Engineer job openings in Atlanta, GA as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 13% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $124,738 per year, or $60 per hour.

Snowflake DATA Engineer

Futran Tech Solutions Pvt. Ltd.

Atlanta, GA • On-site

$110K - $132K/yr

Full-time

Re-posted 23 days ago

Job description

Job Title: Data Engineer
Responsibilities:
• Data Pipeline Development: Design, build, and maintain scalable ETL pipelines to ensure efficient data flow from diverse sources into data warehouses.
• Workflow Automation: Utilize Apache Airflow for scheduling and managing complex data workflows.
• Collaboration: Partner with data scientists to implement machine learning models (MLOps) in production.
• Coding and Scripting: Write high-quality Python code for data extraction, transformation, and loading tasks.
• Big Data Processing: Use Databricks for processing large datasets and optimizing data workflows.
• Data Quality Management: Ensure data accuracy and integrity by implementing monitoring, logging, and ing mechanisms.
• Version Control: Use GitHub for code management and collaboration, maintaining clear documentation of data processes.
• Database Management: Work with Snowflake and BigQuery for data storage and analytics, ensuring optimal performance and scalability.
• Performance Optimization: Analyze and tune data storage solutions and SQL queries for efficiency.
• Cross-Functional Support: Collaborate with business units to identify and fulfill data needs effectively.
Basic Qualifications:
• 3+ years of experience in data engineering or a related field.
• Proficient in Python for data processing and scripting.
• Experience with Apache Airflow or similar workflow management tools.
• Familiarity with machine learning operations (MLOps) and model deployment.
• Solid understanding of cloud platforms (AWS, Azure, GCP).
• Strong SQL skills and experience with data modeling.
• Practical experience with Snowflake and BigQuery.
Preferred Skills:
• Knowledge of Databricks for data processing and analytics.
• Experience with data visualization tools like Tableau or Power BI.
• Familiarity with containerization technologies such as Docker.
• Understanding of DataOps principles and practices.

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