Lead Data Engineer

As a Lead Data Engineer, your role will involve transforming raw data into valuable insights by designing, developing, and maintaining robust data pipelines and infrastructure to support your organization's analytics and decision-making processes. Key Responsibilities: - Build and maintain scalable data pipelines for extracting, transforming, and loading data from various sources into data warehouses or data lakes. - Design, implement, and manage data infrastructure components such as data warehouses, data lakes, and data marts. - Ensure data quality through the implementation of data validation, cleansing, and standardization processes. - Manage the data engineering team effectively. - Optimize data pipelines and infrastructure for improved performance and efficiency. - Collaborate with data analysts, scientists, and business stakeholders to understand data requirements and translate them into technical specifications. - Evaluate and select appropriate data engineering tools and technologies like SQL, Python, Spark, Hadoop, and cloud platforms. - Create and maintain comprehensive documentation for data pipelines, infrastructure, and processes. Qualifications Required: - Strong proficiency in SQL and at least one programming language (e.g., Python, Java). - Experience with data warehousing and data lake technologies (e.g., Snowflake, AWS Redshift, Databricks). - Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and cloud-based data services. - Understanding of data modeling and data architecture concepts. - Experience with ETL/ELT tools and frameworks. - Excellent problem-solving and analytical skills. - Ability to work independently and as part of a team. Preferred Qualifications: - Experience with real-time data processing and streaming technologies (e.g., Kafka, Flink). - Knowledge of machine learning and artificial intelligence concepts. - Experience with data visualization tools (e.g., Tableau, Power BI). - Certification in cloud platforms or data engineering. As a Lead Data Engineer, your role will involve transforming raw data into valuable insights by designing, developing, and maintaining robust data pipelines and infrastructure to support your organization's analytics and decision-making processes. Key Responsibilities: - Build and maintain scalable data pipelines for extracting, transforming, and loading data from various sources into data warehouses or data lakes. - Design, implement, and manage data infrastructure components such as data warehouses, data lakes, and data marts. - Ensure data quality through the implementation of data validation, cleansing, and standardization processes. - Manage the data engineering team effectively. - Optimize data pipelines and infrastructure for improved performance and efficiency. - Collaborate with data analysts, scientists, and business stakeholders to understand data requirements and translate them into technical specifications. - Evaluate and select appropriate data engineering tools and technologies like SQL, Python, Spark, Hadoop, and cloud platforms. - Create and maintain comprehensive documentation for data pipelines, infrastructure, and processes. Qualifications Required: - Strong proficiency in SQL and at least one programming language (e.g., Python, Java). - Experience with data warehousing and data lake technologies (e.g., Snowflake, AWS Redshift, Databricks). - Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and cloud-based data services. - Understanding of data modeling and data architecture concepts. - Experience with ETL/ELT tools and frameworks. - Excellent problem-solving and analytical skills. - Ability to work independently and as part of a team. Preferred Qualifications: - Experience with real-time data processing and streaming technologies (e.g., Kafka, Flink). - Knowledge of machine learning and artificial intelligence concepts. - Experience with data visualization tools (e.g., Tableau, Power BI). - Certification in cloud platforms or data engineering.