Advanced Data Analytics Executive

PRINCIPAL ACCOUNTABILITIES:

Big Data Engineering Management & Implementation

1. Requirement Gathering & Planning

• Requirement Gathering: Capture, consolidate, and document data engineering requirements from various business units.
• Business Case Assessment: Assess and document the benefits of data engineering initiatives (e.g., value, complexity, technology, effort, external vendor) to ensure business needs are met while optimizing resources.

2. Design & Development

• Data Mining & Transformation: Perform data mining on various data attributes and transform them into actionable insights and recommendations using tools like Databricks, and SQL.
• Data Modelling: Design and implement data models to support data warehousing and analytics.
• ETL Processes: Develop and maintain ETL (Extract, Transform, Load) processes to ensure data is accurately and efficiently moved between systems.

3. Implementation

• Data Visualization: Develop seamless UX data visualizations to communicate analytic results, including data maps, to business and functional leaders using tools like Power BI or Python.
• Ad-Hoc Data Analysis: Conduct data analysis on a case-by-case basis according to ad-hoc management requirements using Excel, SQL, Python, and Microsoft Fabric.
• Power Platform Integration: Utilize Power Apps and Power Automate to streamline workflows and automate processes.
• Data Integration: Integrate data from various sources, including APIs and third-party systems, to ensure comprehensive data availability.

4. Maintenance & Support

• Solution Maintenance & Support: Execute activities required to maintain and support in-house developed data engineering solutions, allowing continuous enhancements to meet evolving business needs.
• Data Quality & Governance: Ensure data quality and governance standards are met, including data cleansing and validation processes.
• Documentation: Maintain thorough documentation of data engineering processes, models, and solutions to ensure knowledge sharing and continuity.

Use Case Generation

Identify Use Cases: Discover and document data science use cases for prototypes, demos, and awareness sessions.

Inventory Management: Maintain and update a repository of data analytics/ data science use cases to serve as references and to scale across other departments or business units where applicable.

MVP Development: Create Minimum Viable Products (MVPs) for selected use cases to assess their feasibility and potential value to the business.

Support & Recommendations: Assist in providing recommendations and advice on suitable data science solutions to address business challenges and pain points.

EXPERIENCE:

Fresh graduate can be considered for this role. Candidates with relevant experiences will be added advantage:

• Cloud-Based Solutions: Proven track record in developing and managing innovative cloud-based data solutions (AWS/Azure/GCP/Alibaba Cloud etc).
• Data Management: Extensive hands-on experience in big data management, data warehousing, and data governance.
• Project Management: Demonstrated ability to support data project management through planning, execution, and successful delivery.
• Collaboration: Strong team player with the ability to work collaboratively with cross-functional teams, including data scientists, analysts, and business stakeholders.
• Data Analytics: Expertise in data analytics, particularly in finance and management reporting, along with advanced Excel financial modelling skills.
• Data Analytics Solutions: Skilled in developing and implementing cutting-edge data analytics solutions (SQL, Python, R, VB, JavaScript, Power BI).
• Financial Insights: Experience in involvement in financial insights and management accounting (SAP)
• Application Development: Proficiency in developing both cloud-based and standalone (desktop-based) applications.

QUALIFICATION:

Educational Background:

• Bachelor’s degree in Science, Technology, Engineering, Mathematics, Finance, Accounting, Data Science, Data Analytics, or a related field. Academic exposure to accounting, finance, investment, or business is advantageous.
• A Master’s degree or professional accounting qualification (e.g., CIMA, CFA, MBA, MSc) is an added advantage.

Technical Skills:

• Knowledge or certification in statistics and data analysis.
• Familiarity with ERP Financial Solutions, particularly SAP FICO & BPC, is highly preferred.

Additional Qualifications:

• Strong analytical and problem-solving skills.
• Ability to work collaboratively in a team environment.
• Excellent communication skills to effectively convey technical information to non-technical stakeholders.
• Proficiency in data visualisation tools and techniques.
• Experience with data mining, transformation, and modelling