Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
1-3 years of experience in Data Engineering, DataOps, ML Ops, or similar role.
Strong proficiency in Python and SQL.
Hands-on experience building and maintaining ETL/ELT pipelines.
Experience with Snowflake, Databricks, or equivalent data platforms.
Familiarity with AWS or similar cloud platforms.
Experience with Kubernetes or equivalent container orchestration technologies.
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.
Deploy and manage data workloads on AWS or similar cloud platforms.
Support and maintain Kubernetes-based deployments and related cloud services.
Monitor platform performance and troubleshoot production issues.
Collaborate with Data Scientists to operationalize machine learning models and AI solutions.
Benefits
Collaborative work environment with cross-functional teams.
Opportunities for continuous learning and professional development.
Exposure to cutting-edge technologies in AI and ML.
Flexible work arrangements to support work-life balance.
Full Job Description
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
Data Engineering
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.
Cloud & Platform Engineering
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.
AI/ML Operations
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.
Collaboration & Continuous Improvement
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.
Required Qualifications
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.
Preferred Qualifications
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.
