Member of Technical Staff (Software Engineer, Data Platform)
Posted Updated
- 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
- 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 architectural decisions across storage, compute, orchestration, and data APIs, partnering closely with product engineering and data science to align the data ecosystem with Perplexity’s roadmap
- Mentor engineers, review designs, and raise the technical bar for data infrastructure through thoughtful feedback, documentation, and hands-on collaboration
- Deep familiarity with data orchestration systems (Airflow, Dagster, or similar)
- Familiarity with data quality, lineage, observability, and governance tooling
- Strong systems thinking around reliability, latency, cost, and complexity tradeoffs
- 5+ years (Senior) or 8+ years (Staff) of software engineering experience
- Strong experience building production data infrastructure systems
- Experience supporting ML/AI workflows, training pipelines, or evaluation systems
- Hands-on experience with batch and/or streaming data processing at scale
- Prior ownership of internal platforms used by many teams
- Proficiency in Python and at least one additional backend language (Go, TypeScript, etc.)
- If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above