Job description

The Privacy and Data Protection Office (PDPO) leads and supports TikTok's global privacy efforts, making sure privacy is protected across our products, platforms, and technologies. As a technical privacy center of excellence, we partner closely with engineering, product, legal, and compliance teams to identify privacy risks and design effective technical safeguards throughout the software development lifecycle. At TikTok's scale, protecting user data means going beyond compliance to find complex privacy vulnerabilities and emerging risks across highly distributed products and systems. We combine privacy engineering, security engineering, penetration testing, red teaming, and secure code review to surface those risks, develop mitigations, and build scalable automated assessment capabilities that protect privacy without slowing product innovation. You will work at the intersection of privacy, security, and engineering, on real-world privacy challenges at global scale, influencing how privacy is built into products from the ground up. The Privacy and Data Protection Office (PDPO) leads, supervises, and empowers all of TikTok's privacy work in an accountable and industry-leading way. The team is the in-house expert on the privacy risk landscape and partners across the company to implement the safeguards and technical mitigations that ensure users' privacy is honoured across TikTok's products and platforms. The Offense and Defense team establishes capability to proactively identify, monitor, and mitigate privacy risks across target domains; build a scalable monitoring system as the second line of defense; translate risk objectives into detection capabilities to enhance visibility across key privacy risk areas; and strengthen the technical and data foundation to support reliable monitoring, investigation, and risk-informed decision-making across the organization. - Design, construct, test, and maintain robust, fault-tolerant, and scalable data pipelines and data services to support high-throughput analytical workloads. - Build, optimize, and expand storage solutions, data models, and enterprise data warehouses using modern distributed databases and caching technologies. - Investigate and integrate up-and-coming big data tools, open-source frameworks, and modern technologies into existing production environments. - Implement fault-tolerant mechanisms, triage and debug complex data infrastructure issues, and establish operational monitoring to protect system reliability and SLA. - Partner with data scientists, analysts, product managers, and software engineering teams to align data platform capabilities with core business goals and deliver seamless integrations with third-party data systems. Minimum Qualification(s) Must have 1 year of experience in each of the following: - Designing and modeling data warehouses to power business intelligence, analytics, reporting, and data applications. - Implementing production ETL workflows and data processing using Hive, Spark, Hadoop, and/or SQL Server to optimize execution performance and query latency. - Developing production code using SQL, Python, and/or Java. - Troubleshooting, triaging, and debugging complex distributed data infrastructure and pipeline failures. - Building scalable data services or serving layers utilizing HBase, Elasticsearch, ClickHouse, and/or SQL Server. Preferred Qualification(s) - Strong analytical thinking with a track record of thriving in fast-paced, rapidly evolving environments and taking ownership of ambiguous technical problems. - Excellent communication and cross-team execution skills in a fast-paced environment.

Requirements

Must-haves

You will work at the intersection of privacy, security, and engineering, on real-world privacy challenges at global scale, influencing how privacy is built into products from the ground up.
Design, construct, test, and maintain robust, fault-tolerant, and scalable data pipelines and data services to support high-throughput analytical workloads.
Build, optimize, and expand storage solutions, data models, and enterprise data warehouses using modern distributed databases and caching technologies.
Investigate and integrate up-and-coming big data tools, open-source frameworks, and modern technologies into existing production environments.
Implement fault-tolerant mechanisms, triage and debug complex data infrastructure issues, and establish operational monitoring to protect system reliability and SLA.
Partner with data scientists, analysts, product managers, and software engineering teams to align data platform capabilities with core business goals and deliver seamless integrations with third-party data systems.
Minimum Qualification(s)
Must have 1 year of experience in each of the following:
Designing and modeling data warehouses to power business intelligence, analytics, reporting, and data applications.
Implementing production ETL workflows and data processing using Hive, Spark, Hadoop, and/or SQL Server to optimize execution performance and query latency.
Developing production code using SQL, Python, and/or Java.
Troubleshooting, triaging, and debugging complex distributed data infrastructure and pipeline failures.
Building scalable data services or serving layers utilizing HBase, Elasticsearch, ClickHouse, and/or SQL Server.
Strong analytical thinking with a track record of thriving in fast-paced, rapidly evolving environments and taking ownership of ambiguous technical problems.
Excellent communication and cross-team execution skills in a fast-paced environment.

Nice-to-haves

Preferred Qualification(s)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonjava

Platform: tiktok
Effort: medium