ResponsibilitiesTikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo. Why Join Us: Creation is the core of TikTok's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. To us, every challenge, no matter how ambiguous, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the users we serve. About Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively. The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion. We are looking for an analytics engineer who will power our mission by building state-of-the-art data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals. You will be deploying your data engineering and analytics skills to be part of the mission to build state-of-the-art data products for the audit team. 1. Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data analysts and partners to maintain completeness and accuracy of these assets. 2. Master the data tools and systems inventory across the company, by being an expert and trainer on the team, in data infrastructure, data applications, and data warehouses. 3. Automation and self-service analytics: partner with data analysts and auditors to build and maintain the data warehouse and efficient data models that power key risk indicators dashboards and other data solutions, in support of continuous auditing data strategy. 4. Develop end-to-end AI enabled tools and solutions (incl. front end interface) that automate the evaluation of the design and effectiveness of controls, as well as improve the efficiency of audit field work. 5. Data training and democracy: Systematically organize the relationship between business processes, risks, and data, as well as provide comprehensive and meaningful data democracy to empower the audit team to derive insights. 6. Stakeholder Relationships: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals. 7. Data Analytics Services: Partner with data analysts and auditors to provide data engineering support and guidance for audit engagements, including observing systems and operations, developing queries/ETL, deploying data quality checks to ensure completeness and accuracy for data sets, and facilitating data analysts in deriving insights. 8. Professional Development: Continue to develop and expand knowledge in data engineering practices, machine learning, AI, and ByteDance products through continuous education.

Minimum Qualifications1. Strong proficiency in SQL and at least one mainstream programming language (Python or R). 2. Experience with data integration, ETL processes, and large-scale data processing systems.3. Working knowledge of cloud-based infrastructure such as AWS, GCP, Azure or Snowflake. 4. Experience in implementation of data quality checks or data observability platforms. 5. Experience building and maintaining data products practiced in one or more of the following areas: data infra for product, business or marketing analytics where core metrics/KPIs are developed and monitored continuously; end-to-end data solutions for continuous audit programs, including automating common analyses and recurring checks. 6. Experience in the technology sector, including but not limited to B2C SaaS, media tech, ecommerce, social media platforms, fintech etc. 7. 5+ years practical experience of data data engineering or analytics engineering. Preferred Qualifications and skills1. Bachelor's degree or above in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics. 2. Working knowledge of large scale data processing techniques, such as Hadoop, Flink and MapReduce. 3. Good understanding of data warehouse and data modeling principles. 4. Experience working within a decentralized data environment. 5. Strong business acumen and stakeholder management skills. 6. Good presentation and storytelling skills.