Perplexity
Technology
MemberofTechnicalStaff(SoftwareEngineer,DataPlatform)
Neural analysis suggests this role is
optimal for Senior/Staff candidates.
“Member of Technical Staff (Software Engineer, Data Platform) at Perplexity. Skills: Data Platform, Data Engineering, Streaming Data, Batch Data. Design large-scale data pipelines. Operate large-scale data pipelines”
Industry & Context.
Systems thinking; Troubleshooting
What They're Looking For.
Must Have
5+ years software engineering experience, 8+ years software engineering experience, Production data infrastructure systems experience, Batch and/or streaming data processing experience, Deep familiarity with data orchestration systems, Proficiency in Python, Proficiency in at least one backend language, Systems thinking around reliability, latency, cost, and complexity tradeoffs, Experience supporting ML/AI workflows, Familiarity with data quality, lineage, observability, and governance tooling, Prior ownership of internal platforms
What You'll Do.
Design large-scale data pipelines
Operate large-scale data pipelines
Power product features
Power AI training workflows
Power AI evaluation workflows
Power experimentation
Build event-driven systems
Build streaming systems
Real-time transformation
Build batch frameworks
Handle offline computation
Lead architecture of data orchestration
Own dependency management
Own end-to-end observability
Set guarantees for data correctness
Set guarantees for data freshness
Set guarantees for data lineage
Set guarantees for data recoverability
Design systems for scale growth
Design systems for partial failures
Design systems for evolving schemas
Build self-serve data platforms
Define data contracts
Create data pipelines
Operate data pipelines
Improve developer experience
Provide better abstractions
Provide opinionated paved paths
Set standards for data modeling
Set standards for data testing
Set standards for data validation
Set standards for data deployment
Drive architectural decisions
Partner with product engineering
Partner with data science
Align data ecosystem with roadmap
Raise technical bar for data infrastructure
Provide thoughtful feedback
Provide documentation
Provide hands-on collaboration
How You'll Work.
Team & Collaboration
Product engineering; Data science; Cross-functional teams
Process & Methodology
Roadmap planning
Full Job Description
ABOUT THE ROLE The Data Platform team owns the end-to-end data lifecycle at Perplexity, from ingestion through processing, storage, and serving, powering product features, analytics, experimentation, AI workloads, and the company’s data lake. The team defines the architecture for batch and streaming systems, the orchestration and observability stack, and a self-serve data platform, while thoughtfully combining platforms such as Databricks and Snowflake with open-source technologies including Spark, Kafka, Flink, Airflow, Dagster, dbt, Iceberg, Delta Lake, and ClickHouse. In this senior/staff role, you will shape architecture, set standards, and drive the long-term technical direction of Perplexity’s data ecosystem. KEY RESPONSIBILITIES - Design and operate large-scale batch and streaming data pipelines that directly power Perplexity product features, AI training and evaluation workflows, analytics, and experimentation. - Build event-driven and streaming systems (Kafka, Kinesis, PubSub, or similar) for real-time ingestion, transformation, and delivery, alongside batch frameworks for backfills, aggregations, and offline computation. - Lead the architecture of data orchestration using tools like Airflow or Dagster, owning scheduling, dependency management, retries, SLAs, and end-to-end observability for critical data flows. - Set and enforce guarantees for data correctness, freshness, lineage, and recoverability, designing systems that handle rapid scale growth, partial failures, and evolving schemas without disrupting AI workloads or product experiences. - Build self-serve data platforms that let engineers, data scientists, and analysts safely discover data, define contracts, and create and operate their own pipelines with minimal friction. - Improve developer experience through better abstractions, opinionated paved paths, and standards for data modeling, testing, validation, and deployment, treating the data platform as a product used by many teams. - Drive architec
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