Data Engineer

Role Overview: At PwC, your role in data and analytics engineering will involve leveraging advanced technologies and techniques to design and develop robust data solutions for clients. You will play a crucial part in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Additionally, in data and automation at PwC, you will focus on automating data internally using automation tools or software to streamline data-related processes within the organization. Your responsibilities will include automating data collection, data analysis, data visualization, and other tasks related to handling and processing data. Key Responsibilities: - Data Engineering & Modeling: - Lead the development, optimization, and maintenance of complex data pipelines for large-scale data integration and transformation. - Apply advanced SQL and Python to design, build, and optimize data processing systems and workflows. - Utilize strong data modeling skills to develop and implement efficient and scalable database structures. - Ensure data quality, accuracy, and security throughout all stages of data processing. - Cloud Data Solutions: - Work extensively with cloud platforms (e.g., AWS, Azure, Google Cloud) to design and deploy scalable data solutions. - Implement cloud-based data lakes, warehouses, and other data storage solutions in collaboration with data architects and cloud engineers. - Automate and optimize cloud infrastructure to enhance performance and reduce costs. - Machine Learning & AI Support: - Support data scientists and machine learning engineers by preparing data for machine learning and AI models. - Utilize basic knowledge of machine learning concepts to identify opportunities for predictive analytics and automation within data workflows. - Data Visualization: - Provide clean, structured data to business stakeholders for use in visualizations with Power BI, Tableau, QuickSight, and other tools. - Collaborate with analysts and business users to deliver data that meets their reporting and analytics needs. - Collaboration & Leadership: - Mentor junior data engineers and provide technical leadership in designing data solutions. - Work closely with cross-functional teams including data scientists, cloud architects, and business analysts to ensure data strategies align with business objectives. - Troubleshoot, optimize, and resolve issues in existing data infrastructure and pipelines. Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, Information Technology, or related fields. - Demonstrated Experience in building and optimizing data pipelines in cloud environments. - Ability to lead technical discussions and mentor junior team members. - Strong communication skills and Experience collaborating with cross-functional teams. Role Overview: At PwC, your role in data and analytics engineering will involve leveraging advanced technologies and techniques to design and develop robust data solutions for clients. You will play a crucial part in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Additionally, in data and automation at PwC, you will focus on automating data internally using automation tools or software to streamline data-related processes within the organization. Your responsibilities will include automating data collection, data analysis, data visualization, and other tasks related to handling and processing data. Key Responsibilities: - Data Engineering & Modeling: - Lead the development, optimization, and maintenance of complex data pipelines for large-scale data integration and transformation. - Apply advanced SQL and Python to design, build, and optimize data processing systems and workflows. - Utilize strong data modeling skills to develop and implement efficient and scalable database structures. - Ensure data quality, accuracy, and security throughout all stages of data processing. - Cloud Data Solutions: - Work extensively with cloud platforms (e.g., AWS, Azure, Google Cloud) to design and deploy scalable data solutions. - Implement cloud-based data lakes, warehouses, and other data storage solutions in collaboration with data architects and cloud engineers. - Automate and optimize cloud infrastructure to enhance performance and reduce costs. - Machine Learning & AI Support: - Support data scientists and machine learning engineers by preparing data for machine learning and AI models. - Utilize basic knowledge of machine learning concepts to identify opportunities for predictive analytics and automation within data workflows. - Data Visualization: - Provide clean, structured data to business stakeholders for use in visualizations with Power BI, Tableau, QuickSight, and other tools. - Collaborate with analysts and business users to deliver data that meets their reporting and analytics needs. - Collaboration & Leaders