Senior Associate Data Engineer (RMS)(PwC Acceleration Center Kuala Lumpur)

Industry/Sector

Specialism

Management Level

Job Description & Summary

Focused on relationships you are building meaningful client connections and learning how to manage and inspire others. Navigating increasingly complex situations you are growing your personal brand deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients and to deliver quality. Embracing increased ambiguity you are comfortable when the path forward isnt clear you ask questions and you use these moments as opportunities to grow.

Examples of the skills knowledge and experiences you need to lead and deliver value at this level include but are not limited to:

• Respond effectively to the diverse perspectives needs and feelings of others.
• Use a broad range of tools methodologies and techniques to generate new ideas and solve problems.
• Use critical thinking to break down complex concepts.
• Understand the broader objectives of your project or role and how your work fits into the overall strategy.
• Develop a deeper understanding of the business context and how it is changing.
• Use reflection to develop self awareness enhance strengths and address development areas.
• Interpret data to inform insights and recommendations.
• Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance) the Firms code of conduct and independence requirements.

Key Responsibilities

Data Engineering & Modeling:

• Lead the development optimization and maintenance of complex data pipelines to handle 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.

Analytics Enablement & Financial Modeling

• Prepare and transform datasets to support advanced analytics machine learning and actuarial modeling.

• Apply knowledge of ALM cash flow modeling discounting and regulatory frameworks such as IFRS 17 Solvency II and RBC.

• Support the development of financial models including stochastic modeling scenario testing and DCF techniques.

Collaboration & Leadership

• Work closely with internal and external stakeholders to understand data requirements and deliver customized solutions.

• Mentor junior data engineers and contribute to team knowledge sharing.

• Participate in reviews and provide guidance on technical best practices and quality assurance.

Required Qualifications & Skills

Educational Background

• Bachelors or Masters degree in:

  • Financial Engineering Accounting Finance Applied Finance Investment Banking Economics Statistics Actuarial/Financial Mathematics Computer Science Data Science or other quantitative fields.

Technical Proficiency

• 4 years of experience in data engineering with strong expertise in Python and SQL.

• Strong hands-on experience in:

  • Python (or equivalent programming languages)

  • SQL Excel and VBA

  • Data engineering tools (ETL Spark Databricks Airflow)

  • Cloud platforms (AWS Azure Google Cloud)

• Solid understanding of data foundations including:

  • Data modeling relational database design normalization referential integrity and data warehousing principles

• Familiarity with database design big data processing and data visualization tools (Power BI Tableau QuickSight).

• Experience with developing GenAI solutions including prompt engineering retrieval-augmented generation (RAG) fine-tuning AI agents and multi-agent systems (MAS) is an added advantage.

Domain Knowledge

• Solid understanding of insurance and financial services industries.

• Familiarity with insurance liabilities investment strategies and risk management frameworks.

• Experience working alongside actuarial and finance teams is a strong advantage.

Certifications

• Professional certifications such as FRM or CFA or currently pursuing these will be an added advantage.

Soft Skills

• Strong analytical and problem-solving skills with attention to detail.

• Excellent communication and interpersonal skills.

• Proven ability to manage time and work effectively in fast-paced team-oriented environments.

• A proactive mindset with a passion for continuous learning and professional growth.

Travel Requirements

Job Posting End Date