AI and Data Engineer – Finance Enterprise Data Solutions

Intel is hiring an AI and Data Engineer – Finance Enterprise Data Solutions. Level rates it ; you can apply on Level.

AI in this role

Design and scale cloud-based data architectures, robust pipelines, and AI solutions for enterprise finance analytics.

databrickspythonsqlazuresnowflakebigqueryazure-data-factory

ml-opsdata-engineeringdata-warehousingetlmachine-learningcloud-computingai-solutions

Job Details:

Job Description:

We are seeking a passionate, innovative, and highly skilled AI and Data Engineer to join Intel IT AI and Data Analytics within the Finance Data and Analytics team. In this role, you will help design, develop, and scale cloud based data and AI solutions that enable enterprise analytics and intelligent decision making. You will build and optimize modern data architectures, engineer robust data pipelines, and deliver AI powered insights using technologies such as Microsoft Azure, Snowflake, Databricks, Google Enterprise, and other leading cloud and AI platforms. With a strong focus on finance data domains, you will play a key role in supporting Intel's compliance and transformation objectives while empowering business stakeholders with near real time data driven insights. Your contributions will directly advance business outcomes, operational excellence, and Intel's transformation journey toward the New Intel.

Qualifications:

The ideal candidate should possess: • Bachelor's or Master's degree in Data Science, Computer Science, Information Systems, Engineering, Analytics, or a related quantitative discipline. • 3 to 6 years of proven experience in data engineering, analytics, and data solution delivery within large-scale enterprise environments. • Strong proficiency in SQL and Python, with the ability to design, develop, optimize, and support scalable data pipelines and analytical solutions. • Hands-on experience with modern cloud-based data platforms and services, including Azure, Azure Data Factory (ADF), Databricks, Snowflake, BigQuery, and related data ecosystem technologies. • Solid understanding of data architecture principles, including data warehousing, data lakes, lakehouse architectures, data modeling, ETL/ELT frameworks, and data integration best practices. • Experience designing, building, and maintaining scalable, reliable, and secure data products that support business intelligence, advanced analytics, and AI-driven solutions. • Strong understanding of Artificial Intelligence (AI), Generative AI, and AI-assisted productivity tools, with the ability to apply AI responsibly, securely, and ethically in accordance with enterprise governance standards. • Demonstrated ability to leverage AI technologies to improve productivity, automate repetitive processes, enhance data-driven decision making, and accelerate solution development and delivery. • Experience developing dashboards, reports, and analytical solutions using enterprise BI platforms such as Power BI, Sigma Reporting, SAP BusinessObjects (BOBJ), Web Intelligence (WebI), or equivalent reporting tools. • Knowledge of SAP enterprise data platforms, including SAP ECC, SAP BW, SAP S/4HANA, and SAP HANA, is highly desirable. • Exposure to Machine Learning Operations (MLOps), AIOps, data observability, model lifecycle management, or AI solution deployment practices is an added advantage. • Strong business and data domain acumen, particularly in Finance, Accounting, Treasury, Supply Chain, or related enterprise functions, is preferred. • Experience collaborating within Agile teams and applying Agile delivery methodologies, including sprint planning, backlog management, daily stand-ups, reviews, and retrospectives. • Excellent analytical, problem-solving, communication, and stakeholder management skills, with the ability to translate business requirements into scalable data and AI solutions. Preferred Personal Traits: • Analytical and Detail-Oriented: Excellent problem-solving skills with a keen eye for accuracy and data integrity. • Collaborative Communicator: Strong ability to work cross-functionally, bridging gaps between technical and business teams through clear communication. • Data-Driven Mindset: Passionate about transforming data into actionable insights to support strategic decisions. • Resilient and Adaptable: Thrives in fast-paced, high-pressure environments, adept at multitasking and prioritizing effectively.

Job Type:

Experienced Hire

Shift:

Shift 1 (Malaysia)

Primary Location:

Malaysia, Penang

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

How we rate this

AI and Data Engineer – Finance Enterprise Data Solutions at Intel rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

AI and Data Engineer – Finance Enterprise Data Solutions is an open job at Intel.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in Malaysia, Penang.
The job was posted on October 13, 2026.
Seniority: mid.
Applications are handled by Intel. Apply on Intel

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsData EngineeringData WarehousingETLMachine learningCloud ComputingAI SolutionsDatabricks

Also common in data engineer jobs at this level

AWS Spark Airflow Google Cloud LLM Kafka

Pay

Typical data engineer job: $146k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where data warehousing was part of your work. What did you do?
Tell me about a project where etl was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: ML Ops, Data Engineering, Data Warehousing, ETL, and Machine learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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