Data Engineer with ML Engineering Experience

Highbrow LLC; Atlanta (GA); On-site; USD 100,000 - 130,000; Full time

Job summary

A leading technology firm is seeking a Data Engineer with machine learning engineering experience in Atlanta. The ideal candidate will have over 5 years of data engineering experience and a proficiency in big data tools. Responsibilities include building data pipelines, collaborating with teams, and ensuring data best practices. This role offers opportunities for continuous learning and growth in a dynamic environment.

Qualifications

• 5+ years of experience in data engineering, with 2-3 years in ML engineering.
• Experience in building and maintaining scalable data pipelines and infrastructure.
• Strong understanding of data governance and quality best practices.

Responsibilities

• Build and optimize data pipelines to support ML workflows.
• Collaborate with data scientists and product teams to align infrastructure.
• Document data workflows and ensure compliance with data regulations.

Skills

Big data frameworks and tools

Data modeling

ETL processes

Data pipeline automation

Python

SQL

Machine Learning frameworks

Docker

Kubernetes

Education

Bachelor’s degree in Computer Science or related field

Master’s degree or relevant certifications

Tools

Apache Spark

Hadoop

Kafka

Airflow

AWS

GCP

Azure

Job description

Job Title :- Data Engineer with ML Engineering Experience

Employment Type :- W2

Duration :- Long Term

Visa Type :- All Visa applicable which are ready for W2

Location- Atlanta, GA (Day-1 Onsite)

Job Description:

Education and Experience

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. A Master’s degree or relevant certifications (e.g., Google Professional Data Engineer) is a plus.
• 5+ years of experience in data engineering, with at least 2-3 years of experience in machine learning engineering or deploying ML models in production.
• Proven experience in building and maintaining scalable data pipelines, data warehouses, and infrastructure to support ML workflows.

Technical Skills:

• Proficiency in big data frameworks and tools such as Apache Spark, Hadoop, Kafka, and Airflow.
• Advanced skills in data modeling, ETL processes, and data pipeline automation, with a focus on performance and scalability.
• Experience with cloud platforms (AWS, GCP, Azure) and their data services, such as AWS Glue, Google BigQuery, or Azure Data Lake.
• Strong programming skills in Python, SQL, and experience with data query optimization.
• Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and libraries for building and testing machine learning models.
• Knowledge of containerization and orchestration tools (Docker, Kubernetes) for deploying and managing ML models in production.

Machine Learning Engineering Skills

• Experience in feature engineering, data preprocessing, and building data pipelines to support ML training and inference.
• Knowledge of MLOps best practices for continuous integration, deployment, and monitoring of ML models in production.
• Familiarity with model lifecycle management tools such as MLflow, TFX, or Databricks to streamline ML workflows.
• Strong understanding of data versioning, reproducibility, and monitoring of ML models to ensure model integrity over time.
• Ability to work with structured and unstructured data, with hands-on experience in NLP, computer vision, or time-series data for machine learning applications.

Data Engineering Skills:

• Proficiency in data storage and warehousing solutions (e.g., Snowflake, Redshift, BigQuery) for scalable data architecture.
• Understanding of data governance, quality, and security best practices, including data lineage and compliance with regulations.
• Experience with data lake architecture and data partitioning strategies to support large-scale data analysis.
• Ability to optimize data infrastructure for low-latency access and high throughput, especially for real-time ML applications.

Communication and Collaboration Skills:

• Strong communication skills with the ability to work closely with data scientists, ML engineers, and product teams to align data infrastructure with business requirements.
• Collaborative mindset, with experience working in cross-functional teams to deliver end-to-end data and ML solutions.
• Ability to document data workflows, pipelines, and ML infrastructure, ensuring transparency and ease of knowledge sharing.
• Proven ability to understand and respond to the needs of diverse stakeholders, from technical teams to business leaders.

Additional Qualifications:

• Familiarity with A/B testing, experimentation frameworks, and data-driven evaluation of ML models.
• Knowledge of data privacy and security regulations (e.g., GDPR, CCPA) for responsible data management and ML practices.
• Experience in specific industries like Telcomunications is a plus.
• Passion for staying up-to-date on the latest in data engineering, ML tools, and techniques, with a proactive approach to continuous learning.