Description:

Looking forward to a data-driven career in a dynamic environment? KPMG is now on the lookout for young talents to join our Lighthouse Data & Analytics Apprenticeship Program:

“Discover, Analyse, Transform and Accomplish”

Unlike a mundane internship program, our Apprenticeship program is specifically designed to blend hands-on experience with relevant trainings to enhance your job competency. As part of the Firm’s talent development program, we are committed to invest in data science training for our apprentices to achieve the necessary accreditations.

Your mission is to analyse large data sets and transform them into meaningful insights to identify current trends, cycles and anomalies. Our Apprenticeship program comes with a unique feature of one-to-one mentorship where you will be guided by an assigned mentor throughout the program. Upon completion of the program, you'll stand a chance to be offered a permanent role in KPMG Lighthouse.

If you are passionate about your career path of becoming a data professional, we welcome you to embark the journey with us!

Inspire Confidence. Empower Change.

KPMG the Clear Choice

Responsibilities & Skills:

This program aims to equip candidates with the necessary skills in the data and analytics space. At the end of this program, candidates can expect to have technical and functional capabilities which are essential for Data Engineer role. Some of the key responsibilities of Data Engineer Apprentice are as follows: -

• Assist in creation and enhancement of data solutions enabling seamless delivery of data and for collecting, analysing and managing large sets of data
• Assist in design, develop, automates, and support the use of applications to extract, transform, and load (“ETL”) data
• Assist in the design of the logical data model and implements the physical database structure and constructs and implements operational data stores and data marts
• Participate in consulting with technical lead, development teams and client to get a big-picture idea of the client’s data storage needs
• Assist in designing and coding the data warehousing system to desired project’s specifications
• Assist in conducting preliminary testing of the warehousing environment before data is extracted
• Troubleshooting any issues that may arise
• Present ideas and solutions based on relevant project needs
• Apply business strategy while driving technology strategy, balancing short term and long term needs to ensure that the architecture can scale and evolve accordingly
• Participate and contribute on an end-to-end approach by connecting all the pieces of data to deliver the data while leveraging all available assets
• Provide input, ideas and observations to carry out plans and procedures to ensure schedule attainment, performance and targets are met.

Requirements:

• Availability of 6 months and above.
• Bachelor’s degree with a quantitative focus: Actuarial Science, Business Analytics, Computer Science, Data Science, Information Systems/Technologies (IS/IT), or Business and Commerce with D&A background.
• Good knowledge on implementation of business data solutions (e.g. ETL, data modelling, and data analysis).
• Systematic troubleshooting approach, coupled with strong communication skills.
• Independent, self-motivated, organised, results oriented, teamwork spirit and ability to undertake multiple tasks to meet challenging deadlines.
• Prior experience or project knowledge of data analytics or big data project will be an added advantage.
• Comfortable to work with large and complex dataset.
• Basic knowledge on Office 365 such as MS Word, MS PowerPoint, MS Excel, MS Power Platform
• Good command of English, both written and spoken.

Good to have:

• Data Warehousing, BI and Azure solutions or relevant cloud technologies
• ETL Solution (SSIS and Azure Date Factory or equivalent tools)
• EDW Solution (SSMS and Azure Data Lake, Azure Synapse Analytics or equivalent tools)
• Cube Solution (SSAS or Azure Analysis Service or equivalent tools)
• Data architecture, including data model design and implementation (incl. relational, dimensional, No-SQL)
• Architectural governance of solutions
• Strong theoretical understanding of data management best practice (e.g. data quality, metadata management, data lineage)
• Proficiency in warehousing architecture techniques including MOLAP, ROLAP, ODS, DM, and EDW.