Senior Manager_Delivery Manager_Data & Analytics_Advisory

Not Applicable Data, Analytics & AI Senior Manager At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.

In business intelligence at PwC, you will focus on leveraging data and analytics to provide strategic insights and drive informed decisionmaking for clients. You will develop and implement innovative solutions to optimise business performance and enhance competitive advantage.

• Lead the endtoend delivery of data projects, including Data Warehouse, Data Lake, and Lakehouse solutions.
• Develop detailed project plans, allocate resources, and monitor project progress to ensure timely and withinbudget delivery.
• Identify and mitigate risks, ensuring successful project outcomes.

• Provide technical oversight and guidance on best practices in data engineering, cloud architecture, and data management.
• Ensure solutions are scalable, robust, and align with industry standards and client requirements.
• Oversee the design, development, and implementation of data solutions using Azure or AWS and Databricks.

• Engage with clients to understand their business needs and translate them into technical requirements.
• Build and maintain strong relationships with key client stakeholders.
• Present complex technical concepts and solutions in a clear and concise manner to nontechnical stakeholders.

• Lead and mentor a team of data engineers, fostering a collaborative and highperformance culture.
• Provide guidance and support to team members in their professional development and project delivery.
• Ensure the team is equipped with the necessary tools and resources to succeed.

• Develop and implement data pipelines, ETL processes, and data integration solutions using Azure Data Factory, AWS Glue, Databricks, and other relevant tools.
• Optimize data storage and retrieval performance, ensuring data quality and integrity.
• Leverage advanced analytics and machine learning capabilities within Databricks to drive business insights.

• Stay uptodate with the latest advancements in Azure, AWS, Databricks, and data engineering technologies.
• Implement best practices and continuous improvement initiatives to enhance the efficiency and effectiveness of data engineering processes.
• Foster a culture of innovation and experimentation within the team.

• Strong problemsolving and analytical skills.
• Deep technical expertise in Azure, Google or AWS and Databricks.
• Exceptional project management and organizational abilities.
• High level of emotional intelligence and client empathy.
• Proficiency in
  • Concepts of Data warehousing and Data Lake (e.g., SCD1, SCD2, Dimensional Modeling, KPIs and Measures, Data Catalog, Star and Snowflake schema, Delta Table and Delta Live Tables)
  • Data warehousing solutions (e.g., Azure Synapse, Azure SQL, ADLS Gen2, BLOB Storage for Azure and Redshift, S3, AWS Glue AWS Lambda for AWS, Google data management technologies )
  • Data Lake solutions (e.g., MS Fabric, Purview, AWS Lakehouse, BigQuery and BigTable )
  • Lakehouse solutions (e.g., Databricks, Unity Catalog, Python and Pyspark)
  • Data visualization tools (e.g., Power BI, Tableau) is a plus

Degrees/Field of Study required Master of Engineering, Bachelor of Engineering Degrees/Field of Study preferred Project Management Accepting Feedback, Accepting Feedback, Active Listening, Analytical Thinking, Business Case Development, Business Data Analytics, Business Intelligence and Reporting Tools (BIRT), Business Intelligence Development Studio, Coaching and Feedback, Communication, Competitive Advantage, Continuous Process Improvement, Creativity, Data Analysis and Interpretation, Data Architecture, Database Management System (DBMS), Data Collection, Data Pipeline, Data Quality, Data Science, Data Visualization, Embracing Change, Emotional Regulation, Empathy, Inclusion {+ 20 more} No No