Lead Data Engineer - ETL/Power BI

As a Lead Data Engineer at our company, you will be instrumental in turning raw data into actionable insights. Your primary responsibilities will include developing robust data pipelines, managing data infrastructure, ensuring data quality, and optimizing performance. Additionally, you will collaborate with various stakeholders, select appropriate tools and technologies, and document processes effectively. **Key Responsibilities:** - **Data Pipeline Development:** Build and maintain scalable data pipelines for ETL processes from diverse sources. - **Data Infrastructure:** Design, implement, and manage data warehouses, data lakes, and data marts. - **Data Quality:** Implement processes for data validation, cleansing, and standardization. - **Team Management:** Efficiently handle and lead a team. - **Performance Optimization:** Optimize data pipelines and infrastructure for enhanced efficiency. - **Collaboration:** Work closely with data analysts, scientists, and business stakeholders to meet their data requirements. - **Tool and Technology Selection:** Evaluate and choose appropriate data engineering tools and technologies. - **Documentation:** Create and maintain comprehensive documentation for data processes and infrastructure. **Qualifications Required:** - Strong proficiency in SQL and a 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 architecture. - Experience with ETL/ELT tools and frameworks. - Excellent problem-solving and analytical skills. - Ability to work both independently and collaboratively in a team environment. **Preferred Qualifications:** - Experience with real-time data processing and streaming technologies (e.g., Kafka, Flink). - Knowledge of machine learning and artificial intelligence concepts. - Familiarity with data visualization tools (e.g., Tableau, Power BI). - Certification in cloud platforms or data engineering. As a Lead Data Engineer at our company, you will be instrumental in turning raw data into actionable insights. Your primary responsibilities will include developing robust data pipelines, managing data infrastructure, ensuring data quality, and optimizing performance. Additionally, you will collaborate with various stakeholders, select appropriate tools and technologies, and document processes effectively. **Key Responsibilities:** - **Data Pipeline Development:** Build and maintain scalable data pipelines for ETL processes from diverse sources. - **Data Infrastructure:** Design, implement, and manage data warehouses, data lakes, and data marts. - **Data Quality:** Implement processes for data validation, cleansing, and standardization. - **Team Management:** Efficiently handle and lead a team. - **Performance Optimization:** Optimize data pipelines and infrastructure for enhanced efficiency. - **Collaboration:** Work closely with data analysts, scientists, and business stakeholders to meet their data requirements. - **Tool and Technology Selection:** Evaluate and choose appropriate data engineering tools and technologies. - **Documentation:** Create and maintain comprehensive documentation for data processes and infrastructure. **Qualifications Required:** - Strong proficiency in SQL and a 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 architecture. - Experience with ETL/ELT tools and frameworks. - Excellent problem-solving and analytical skills. - Ability to work both independently and collaboratively in a team environment. **Preferred Qualifications:** - Experience with real-time data processing and streaming technologies (e.g., Kafka, Flink). - Knowledge of machine learning and artificial intelligence concepts. - Familiarity with data visualization tools (e.g., Tableau, Power BI). - Certification in cloud platforms or data engineering.