Description

You will architect and optimize data pipelines for structured and unstructured data.

Responsibilities

• Design scalable ETL workflows to support AI/ML models and business intelligence systems.
• Engineer cloud-native data infrastructure on AWS, GCP, or Azure to manage massive datasets.
• Build and maintain data lakes, warehouses, and real-time data streaming solutions.
• Optimize query performance and database architectures for data insights.
• Automate workflows using orchestration tools like Airflow, Luigi, or Prefect.
• Ensure data solutions comply with industry standards and privacy regulations.

Required Skills

• 5+ years of hands-on experience in data engineering or a related field.
• Expertise in Python and SQL.
• Proficiency with distributed data frameworks including Spark, Hadoop, and Kafka.
• Deep understanding of data modeling, schema design, and warehousing using Snowflake, Redshift, or BigQuery.
• Experience with relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, or Cassandra.
• Knowledge of real-time and batch processing using Kafka, Flink, or Spark Streaming.
• Experience with automation, CI/CD, and infrastructure-as-code using Terraform, Docker, or Kubernetes.
• Ability to troubleshoot and optimize complex data workflows.
• Any Graduate degree.

Key Skills

Education

Any Graduate