Data Engineer

The Data Engineer works closely with Data Scientists, Data Analysts, and Business stakeholders to develop reliable data solutions and operationalize machine learning and AI applications.

**Key Responsibilities**

• Design, develop, and maintain scalable **ETL/ELT pipelines** for structured and semi-structured data.
• Build and optimize data warehouses, data lakes, and data models.
• Develop automated data ingestion, transformation, and orchestration workflows.
• Manage and optimize data platforms such as **Snowflake, Databricks, or equivalent**.
• Ensure data quality, reliability, governance, and observability across data pipelines.

• Deploy and manage data workloads on **AWS or similar cloud platforms (Azure/GCP)**.
• Implement infrastructure and application deployments using containerized environments.
• Support and maintain **Kubernetes-based deployments** and related cloud services.
• Monitor platform performance and troubleshoot production issues.

• Support deployment, monitoring, and maintenance of **AI/ML applications and services** in production environments.
• Collaborate with Data Scientists to operationalize machine learning models and AI solutions.
• Implement basic MLOps practices using tools such as **MLflow, SageMaker, Kubeflow, or equivalent platforms**.
• Monitor AI application health, data drift, model performance, and system reliability.

• Work closely with cross-functional teams to gather requirements and deliver scalable solutions.
• Participate in code reviews, documentation, testing, and CI/CD processes.
• Continuously improve platform reliability, security, and operational efficiency.

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
• **1–3 years of experience** in Data Engineering, DataOps, ML Ops, or a related role.
• Strong proficiency in **Python**.
• Strong experience with **SQL** and database optimization.
• Hands-on experience building and maintaining **ETL/ELT pipelines**.
• Experience with **Snowflake, Databricks, or equivalent modern data platforms**.
• Experience with **AWS or similar cloud platforms**.
• Experience with **Kubernetes or equivalent container orchestration technologies**.
• Understanding data warehousing, data lakes, and modern data architectures.
• Experience with Git and software development best practices.

• Experience deploying and supporting **AI/ML applications in production**.
• Familiarity with **LLM/Generative AI application deployment**, inference endpoints, or AI service integration.
• Experience with ML lifecycle tools such as **MLflow, Kubeflow, SageMaker, or Databricks ML**.
• Experience with orchestration tools such as **Airflow, Dagster, or Prefect**.
• Knowledge of Docker and containerized application deployment.
• Familiarity with CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation).
• Exposure to monitoring tools such as Prometheus, Grafana, CloudWatch, or Datadog.
• Experience with streaming technologies such as Kafka is a plus.

*Annual Salary range: $90,000.00 - $95,000.00*