Data Analyst at AvePoint

Kuala Lumpur, Kuala Lumpur, Malaysia -

Full Time

Start Date

Immediate

Expiry Date

04 Jan, 26

Salary

0.0

Posted On

06 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Python, SQL, Data Warehousing, ETL, Data Visualization, Business Intelligence, Automation, Statistical Analysis, Trend Forecasting, Data Modelling, Cloud Platforms, AWS, Pandas, NumPy, Power BI

Industry

Data Security Software Products

Description

Beyond Secure. AvePoint is the global leader in data security, governance, and resilience, going beyond traditional solutions to ensure a robust data foundation and enable organizations everywhere to collaborate with confidence. Over 25,000 customers worldwide rely on the AvePoint Confidence Platform to prepare, secure, and optimize their critical data across Microsoft, Google, Salesforce, and other collaboration environments. AvePoint’s global channel partner program includes approximately 5,000 managed service providers, value-added resellers, and systems integrators, with our solutions available in more than 100 cloud marketplaces. To learn more, visit www.avepoint.com. Overview We are seeking a data-literate and technically strong Data Analyst to join our team. You will be instrumental in building our analytics infrastructure and unlocking deep insights from data. A key aspect of this role involves using Python to automate processes, perform advanced analytics, and enhance our data capabilities beyond traditional SQL and BI tools. Experience & Skills Required Key Responsibilities: End-to-End Data Pipeline Management: Design, develop, and maintain robust, scalable data pipelines from various sources into our data warehouse. Advanced Data Processing & Automation: Use Python (Pandas, NumPy) to build and automate complex data transformation and feature engineering tasks, complementing our core ETL frameworks. Data Warehousing & Modelling: Manage and optimize the data warehouse environment, including designing and implementing efficient data models for business intelligence. Analytics & Insight Generation: Develop scripts and analytical models in Python for statistical analysis, trend forecasting, and uncovering deeper business insights. Visualization & Reporting: Create and maintain interactive dashboards and reports to communicate findings effectively to stakeholders. Collaboration: Partner with business units to translate their needs into technical solutions and data-driven recommendations. Requirements: Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field. 3+ years of professional experience in a Data Analyst, BI Analyst, or similar role. Strong proficiency in SQL for complex data querying and manipulation. Proven experience with Python for data analysis, including using libraries such as Pandas, NumPy, and Requests for data manipulation, analysis, and API interactions. Hands-on experience with cloud data platforms on AWS (e.g., Glue, S3, Redshift). Expertise in at least one major data visualization tool: Power BI, Tableau, or Qlik. Solid understanding of data modelling and ETL/ELT best practices. Preferred Qualifications: Experience in using Python for ETL/ELT automation (e.g., within AWS Glue Jobs, Lambda functions) is a significant advantage. Familiarity with SQL Server Integration Services (SSIS). Knowledge of other AWS services (e.g., IAM, CloudWatch). Exposure to machine learning libraries is a plus. Proven ability to manage multiple projects in a fast-paced environment. If you are looking for an opportunity to grow your career while working on meaningful projects, we would love to hear from you! Any personal data you share with us during the application process will be processed strictly in compliance with applicable data protection laws and our Privacy Notice.

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Responsibilities

The Data Analyst will design, develop, and maintain data pipelines, automate data processing, and generate insights from data. They will also create dashboards and reports to communicate findings to stakeholders.