- Design, develop, and maintain scalable data pipelines.
- Build and manage ETL/ELT workflows.
- Extract, transform, and load data from multiple sources.
- Develop data processing solutions using Python, SQL, and PySpark.
- Work with structured and unstructured data.
- Design and maintain data warehouses and data lakes.
- Develop automated workflows using Apache Airflow.
- Integrate data from APIs, databases, and external systems.
- Ensure data quality, accuracy, and consistency.
- Monitor, troubleshoot, and optimize data pipelines.
- Implement data validation, governance, and security.
- Collaborate with Data Scientists, Data Analysts, and BI teams.

Preferred Skills

- Solid knowledge of Python, SQL, and PySpark.
- Experience with Apache Spark and Hadoop.
- Knowledge of ETL/ELT processes.




- Hands-on experience with Apache Airflow.
- Knowledge of Apache Kafka and real-time data processing.
- Experience with MySQL, PostgreSQL, and MongoDB.
- Knowledge of Snowflake, Databricks, Redshift, or BigQuery.
- Experience with AWS, Azure, or GCP cloud platforms.
- Knowledge of AWS Glue and Azure Data Factory.
- Understanding of Data Warehousing and Data Modelling.
- Experience with Data Lakes and Lakehouse architecture.
- Knowledge of Git, GitHub, and CI/CD pipelines.
- Familiarity with Docker and Kubernetes.
- Knowledge of Linux and Shell scripting.
- Strong problem-solving and analytical skills.

📌 Data Engineer (Canada)
🏢 Spait Infotech
📍 Canada