• Develop and manage ETL/ELT workflows to ensure efficient data processing and availability for AI/ML applications.
  

• Optimize data storage, retrieval, and processing performance across large datasets.
  

• Prepare, clean, and transform datasets required for training, validating, and testing machine learning models.
  

• Collaborate with data scientists and ML engineers to ensure data readiness for model development and experimentation.
  

• Support feature engineering processes and maintain feature stores where applicable.
  

• Build and manage data infrastructure that supports AI/ML workloads, including batch and real-time processing systems.
  

• Implement scalable data architectures using cloud platforms or distributed systems.
  

• Ensure high data availability, integrity, and reliability across data environments.
  

• Implement data validation, monitoring, and quality control processes.
  

• Ensure data governance policies, compliance standards, and security best practices are followed.
  

• Manage metadata, data lineage, and documentation for AI/ML datasets.
  

• Work closely with data scientists, analysts, and software engineers to operationalize machine learning models.
  

• Support deployment of ML models into production environments through robust data infrastructure.
  

• Continuously evaluate new tools, technologies, and frameworks to improve data engineering and AI capabilities.
  

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Artificial Intelligence, or a related field.
  

• 3+ years of experience in data engineering, data platforms, or related technical roles.
  

• Experience working with machine learning pipelines or AI-driven data environments.
  

• Proven experience building scalable data pipelines and managing large datasets.
  

• Strong proficiency in Python, SQL, or Scala.
  

• Experience with data pipeline tools such as Apache Airflow, Kafka, or similar frameworks.
  

• Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and cloud-based data services.
  

• Experience with data warehousing solutions such as Snowflake, BigQuery, or Redshift.
  

• Understanding of machine learning workflows and data preparation techniques.
  

• Strong analytical and problem-solving skills.
  

• Ability to work collaboratively in cross-functional teams.
  

• Good documentation and communication skills.
  

• Strong attention to data quality, security, and governance practices.
  

Benefits