Engineering Manager, Data Platform

San JoseRegularR&DJob ID: A47369 Responsibilities The Data Platform team at the TikTok USDS Joint Venture operates TikTok's US data processing ecosystem: a full suite of big data services, hundreds of thousands of batch and streaming pipelines/tasks, resource optimization, compliance and guaranteed data production. We ensure pipelines produce high-quality production data reliably and on time to meet the need of all the major TikTok businesses lines

• including TikTok, Live, eCommerce, while maintaining efficient resource utilization, workload isolation, and regulatory compliance. About the Role: As an Engineering Manager on the Data Platform team, you will lead a team of data engineers and software engineers, create innovative solutions to scale operations, and own the reliable on-time delivery of large-scale data assets. Your team is the business's dependable delivery partner: our happy clients count on your service to produce data that are critical for business intelligence, application operations, and customer successes across multiple data centers. You will innovate with software, automation, and AI to do more with what we have — driving self-healing pipelines, AI-assisted reliability engineering and on-call, intelligent cost and resource optimization, and automation for data development, quality checks, and governance. You will collaborate closely with the internal Data foundation team, surfacing operational needs, co-defining roadmaps, and jointly turning platform capabilities into measurable reliability, efficiency, and productivity gains for your business line. Responsibilities:
• Lead and grow the team: Manage, coach, and develop a team of data engineers. Foster a culture of ownership, operational excellence, engineering rigor, and a builder's mindset toward automation and AI.
• Own operational delivery for your business line: Take end-to-end accountability for the reliability, timeliness, and quality of batch and streaming pipelines, jobs, and data assets serving the business needs. Own SLAs, on-call health, incident response, and postmortem-driven improvement.
• Innovate with software, automation, and AI: Build or adopt tooling for AI-assisted SRE (triage, root-cause analysis, auto-remediation), self-healing and auto-tuning pipelines, LLM-powered data development and code review, automated data quality and lineage checks, and intelligent cost optimization. Turn repeated toil into products.
• Partner with the business: Act as the primary engineering point of contact for your business line's Product, Analytics, Data Science, and Engineering partners. Translate business priorities into a concrete data delivery and reliability roadmap; communicate trade-offs, risks, and dependencies clearly.
• Collaborate with the Data Foundation team: Co-shape data platform requirements, adopt new platform capabilities, and bring foundation capabilities to your business, and bring your business's realities to the foundation.
• Uphold governance and compliance: Ensure all pipelines, datasets, and tooling under your team meet data governance, privacy, security, and regulatory requirements applicable to the US data environment. Qualifications Minimum Qualifications
• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of experience building and operating enterprise-grade data platforms or large-scale distributed data systems, including 3+ years in an engineering leadership or engineering management role overseeing technical teams in a cross-functional, fast-paced environment.
• Hands-on experience operating production batch and streaming systems built on technologies such as Spark, Flink, Hadoop, Kafka, Druid, ClickHouse, or equivalents — including on-call ownership, SLA management, and incident response. Must have a builder mindset.
• Strong grasp of modern data architectures (data lakes, data warehouses, lakehouse patterns) and of what makes large-scale pipelines reliable, efficient, and observable.
• Demonstrated ability to improve throughput, reliability, or cost efficiency of a data system without linear headcount growth — through automation, tooling, or architectural change.
• Excellent communication, stakeholder management, and cross-functional partnership skills.
• Ability to balance strategic thinking with hands-on problem-solving. Preferred Qualifications
• Experience applying AI/LLM technologies to data operations — for example, AI-assisted SRE and observability, LLM-powered data development or code review, automated governance and cataloging, or intelligent cost optimization.
• Track record leading a business-embedded data engineering team that partners with Product, Analytics, and Data Science on a specific business line (e.g., e-commerce, live streaming, consumer social).
• Familiarity with data compliance and privacy requirements in a regulated environment (e.g., US data sovereignty, PII handling). Job Information 【F