Data Engineer with ML Experience

Data Engineer with ML Experience

Location: Atlanta, GA ( Onsite from Day 1 )

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 23 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, Google Cloud Platform, 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, ScikitLearn) 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 handson experience in NLP, computer vision, or timeseries 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 largescale data analysis.
• Ability to optimize data infrastructure for low latency access and high throughput, especially for Realtime 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 endtoend 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.