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

Robust 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 (Winnipeg)
🏢 Spait Infotech
📍 Winnipeg