Empowers Staffing Inc
Posted 81 days agoVerified live 2d ago
AI Data Engineer (ML Data Pipelines)
Brief overview
Remote
4+ yrsMinimum
PythonSQLSparkDatabricksAirflowData PipelinesData Quality ValidationGreat ExpectationsAWSAzureGCPKafkaFeature EngineeringMachine LearningVersion Control
About the company
Empowers Staffing Incempowersstaffing.com
Welcome to Empowers Staffing – Where Expertise Meets Diversity! We are more than just a staffing company; we are pioneers in the realm of IT, Banking & Finance Recruitment and Staffing, and we’re growing faster than ever before.
Job description
Summary
Empowers Staffing Inc is seeking an AI Data Engineer to design and build production-grade data pipelines that power machine learning systems. This role focuses on creating scalable ingestion, transformation, and feature engineering workflows that support model training, evaluation, and real-time inference.
Responsibilities
- Design and build scalable data pipelines for ML workflows
- Develop feature engineering and data preparation processes
- Implement batch and real-time data ingestion systems
- Ensure data quality, validation, and monitoring
- Collaborate with ML engineers to support model training and deployment
- Integrate pipelines with orchestration tools (Airflow or similar)
- Optimize pipeline performance and cloud cost efficiency
- Maintain documentation and version control of data workflows
Skills
- 4+ years of experience in Data Engineering
- Strong Python and SQL skills
- Experience building data pipelines for ML or analytics systems
- Hands-on experience with Spark, Databricks, or similar distributed processing frameworks
- Experience with orchestration tools (Airflow or similar)
- Experience in AWS, Azure, or GCP environments
- Familiarity with data quality validation and monitoring frameworks
- Understanding of feature engineering and model data lifecycle
- Experience with streaming systems (Kafka, Kinesis, Pub/Sub)
- Experience supporting model deployment and MLOps workflows
- Experience with feature stores or vector databases
- Familiarity with ML frameworks (TensorFlow, PyTorch)
Qualifications
Must Haves
- 4+ years of experience in Data Engineering
- Strong Python and SQL skills
- Experience building data pipelines for ML or analytics systems
- Hands-on experience with Spark, Databricks, or similar distributed processing frameworks
- Experience with orchestration tools (Airflow or similar)
- Experience in AWS, Azure, or GCP environments
- Familiarity with data quality validation and monitoring frameworks
- Understanding of feature engineering and model data lifecycle
Nice to Haves
- Experience with streaming systems (Kafka, Kinesis, Pub/Sub)
- Experience supporting model deployment and MLOps workflows
- Experience with feature stores or vector databases
- Familiarity with ML frameworks (TensorFlow, PyTorch)
