GenAI Python Systems Engineer -Senior Manager

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.

Growing as a strategic advisor, you leverage your influence, expertise, and network to deliver quality results. You motivate and coach others, coming together to solve complex problems. As you increase in autonomy, you apply sound judgment, recognising when to take action and when to escalate. You are expected to solve through complexity, ask thoughtful questions, and clearly communicate how things fit together. Your ability to develop and sustain high performing, diverse, and inclusive teams, and your commitment to excellence, contributes to the success of our Firm.

• Craft and convey clear, impactful and engaging messages that tell a holistic story.
• Apply systems thinking to identify underlying problems and/or opportunities.
• Validate outcomes with clients, share alternative perspectives, and act on client feedback.
• Direct the team through complexity, demonstrating composure through ambiguous, challenging and uncertain situations.
• Deepen and evolve your expertise with a focus on staying relevant.
• Initiate open and honest coaching conversations at all levels.
• Make difficult decisions and take action to resolve issues hindering team effectiveness.
• Model and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.

• Strong proficiency in Python and experience with structured and unstructured data.
• Strong proficiency in SQL and experience with relational databases.
• Experience writing and maintaining FastAPI endpoints for scalable applications.
• Strong understanding of AI techniques that enhance LLMs, such as AI Agents, Retrieval-Augmented Generation (RAG), etc.
• Experience in prompt engineering for optimizing LLM outputs.
• Experience with AI, GenAI, and machine learning and data science workflows.
• Experience with machine learning and data science workflows is a plus.
• Experienced in high software quality through developer-led testing, validation, and best practices.
• Understanding of developer-led quality assurance, including automated testing, performance tuning, and debugging.
• Knowledge of software development workflows and CI/CD pipelines.
• Work with Docker, including writing Docker files and managing containerized deployments.
• Develop and deploy scalable data storage solutions using AWS, Azure, and GCP services such as S3, Redshift, PostGresDB RDS, DynamoDB, Azure Data Lake Storage, Azure Cosmos DB, Azure SQL DB, GCP Cloud Storage, etc.
• Knowledge of data integration solutions using AWS Glue, AWS Lambda, Azure Data Factory, Azure Functions, GCP Functions, GCP Dataproc, Dataflow, and other relevant services.
• Design and manage data warehouses and data lakes, ensuring data is organized and accessible.
• Design and implement comprehensive data architecture strategies that meet the current and future business needs.
• Develop and document data or system models, flow diagrams, and architecture guidelines.
• Ensure data architecture is compliant with data governance and data security policies.
• Collaborate with business stakeholders to understand their data requirements and translate them into technical solutions.
• Evaluate and recommend new data technologies and tools to enhance data architecture.
• Implement IAM roles and policies to manage access and permissions within AWS, Azure, GCP.
• Use AWS CloudFormation, Azure Resource Manager templates, Terraform for infrastructure as code (IaC) deployments.
• Use AWS, Azure, and GCP DevOps services to build and deploy DevOps pipelines.
• Optimize Cloud resources for cost, performance, and scalability.
• Knowledge of data governance and data security best practices.
• Strong analytical, problem-solving, and communication skills.
• Ability to work independently and as part of a team in a fast-paced environment.

• Applying modern, cloud-based technology skills, ability to research emerging trends, analyst publications, and adoption of modern technologies in solution architectures;
• Collaborating and contributing as a team member: understanding personal and team roles, contributing to a positive working environment by building proven relationships with team members, proactively seeking guidance, clarification and feedback;
• Prioritizing and handling multiple tasks, researching and analyzing pertinent client, industry and technical matters, utilizing problem-solving skills, and communicating effectively in written and verbal formats to various audiences (including various levels of management and external clients) in a professional business environment; and,
• Coaching and collaborating with associates who assist with this work, including providing coaching, feedback and guidance on work performance.