Data Platform Tech Lead: AI-Driven Reliability & Delivery

TikTok USDS Joint Venture

San Jose (CA)

On-site

USD 209,000 - 438,000

Full time

4 days ago

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Job summary

TikTok USDS Joint Venture LLC is seeking a Tech Lead Manager for the Data Platform team to guide data and software engineers in delivering reliable, large-scale data assets across multiple data centers.

You will own end-to-end delivery, drive automation and AI-powered reliability, and collaborate with Product, Analytics, Data Science, and Data Foundation to turn platform capabilities into measurable business value. On-site presence in US aligns with strategic needs.

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.

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.