Data engineer

As a Data Engineer on a contract basis, your role will involve the following key responsibilities: - Design, develop, and maintain scalable data pipelines using PySpark and distributed computing frameworks. - Implement ETL processes and integrate data from structured and unstructured sources into cloud data warehouses. - Work across Azure or AWS cloud ecosystems to deploy and manage big data workflows. - Optimize performance of SQL queries and develop stored procedures for data transformation and analytics. - Collaborate with Data Scientists, Analysts, and Business teams to ensure reliable data availability and quality. - Maintain documentation and implement best practices for data architecture, governance, and security. To excel in this role, you should possess the following required skills: - Programming: Proficient in PySpark, Python, and SQL, MongoDB. - Cloud Platforms: Hands-on experience with Azure Data Factory, Databricks, or AWS Glue/Redshift. - Data Engineering Tools: Familiarity with Apache Spark, Kafka, Airflow, or similar tools. - Data Warehousing: Strong knowledge of designing and working with data warehouses like Snowflake, BigQuery, Synapse, or Redshift. - Data Modeling: Experience in dimensional modeling, star/snowflake schema, and data lake architecture. - CI/CD & Version Control: Exposure to Git, Terraform, or other DevOps tools is a plus. In addition, the following preferred qualifications are desirable: - Bachelor's or Master's in Computer Science, Engineering, or related field. - Certifications in Azure/AWS are highly desirable. - Knowledge of business intelligence tools (Power BI, Tableau) is a bonus. As a Data Engineer on a contract basis, your role will involve the following key responsibilities: - Design, develop, and maintain scalable data pipelines using PySpark and distributed computing frameworks. - Implement ETL processes and integrate data from structured and unstructured sources into cloud data warehouses. - Work across Azure or AWS cloud ecosystems to deploy and manage big data workflows. - Optimize performance of SQL queries and develop stored procedures for data transformation and analytics. - Collaborate with Data Scientists, Analysts, and Business teams to ensure reliable data availability and quality. - Maintain documentation and implement best practices for data architecture, governance, and security. To excel in this role, you should possess the following required skills: - Programming: Proficient in PySpark, Python, and SQL, MongoDB. - Cloud Platforms: Hands-on experience with Azure Data Factory, Databricks, or AWS Glue/Redshift. - Data Engineering Tools: Familiarity with Apache Spark, Kafka, Airflow, or similar tools. - Data Warehousing: Strong knowledge of designing and working with data warehouses like Snowflake, BigQuery, Synapse, or Redshift. - Data Modeling: Experience in dimensional modeling, star/snowflake schema, and data lake architecture. - CI/CD & Version Control: Exposure to Git, Terraform, or other DevOps tools is a plus. In addition, the following preferred qualifications are desirable: - Bachelor's or Master's in Computer Science, Engineering, or related field. - Certifications in Azure/AWS are highly desirable. - Knowledge of business intelligence tools (Power BI, Tableau) is a bonus.