Data Engineer

No C2C

We're looking for a hands-on Data Engineer to help build, scale, and fine-tune real-time data systems using Kafka, AWS, and a modern data stack. In this role, you'll work deeply with streaming data, ETL, distributed systems, and PostgreSQL to power analytics, product innovation, and AI-driven use cases. You'll also get to work with AI/ML frameworks, automation, and MLOps tools to support advanced modeling and a highly responsive data platform.

What You'll Do

• Design and build real-time streaming pipelines using Kafka, Confluent Schema Registry, and Zookeeper
• Build and manage cloud-based data workflows using AWS services like Glue, EMR, EC2, and S3
• Optimize and maintain PostgreSQL and other databases with strong schema design, advanced SQL, and performance tuning
• Integrate AI and ML frameworks (TensorFlow, PyTorch, Hugging Face) into data pipelines for training and inference
• Automate data quality checks, feature generation, and anomaly detection using AI-powered monitoring and observability tools
• Partner with ML engineers to deploy, monitor, and continuously improve machine learning models in both batch and real-time pipelines using tools like MLflow, SageMaker, Airflow, and Kubeflow
• Experiment with vector databases and retrieval-augmented generation (RAG) pipelines to support GenAI and LLM initiatives
• Build scalable, cloud-native, event-driven architectures that power AI-driven data products

What You Bring

• Bachelor's degree in Computer Science, Engineering, Math, or a related technical field
• 3+ years of hands-on data engineering experience with Kafka (Confluent or open-source) and AWS
• Experience with automated data quality, monitoring, and observability tools
• Strong SQL skills and solid database fundamentals with PostgreSQL and both traditional and NoSQL databases
• Proficiency in Python, Scala, or Java for pipeline development and AI integrations
• Experience with synthetic data generation, vector databases, or GenAI-powered data products
• Hands-on experience integrating ML models into production data pipelines using frameworks like PyTorch or TensorFlow and MLOps tools such as Airflow, MLflow, SageMaker, or Kubeflow