Member of Technical Staff (Software Engineer, Data Platform)
Perplexity · Not provided by source
MeritLog read this listing from Perplexity's Ashby job board and last checked it on September 12, 2026.
Source: the employer's Ashby job board. Open the original listing for current details.
Job details
- Work model
- Not provided by source
- Salary
- $220K - $405K
- Location
- San Francisco
Hiring context
How this role compares at Perplexity
Perplexity has 115 live roles in MeritLog’s catalog across 11 job families, and 21 of them are in data & analytics. 99 of those listings publish a pay range, a disclosure rate of 86%.
This role's posted range of $220K - $405K sits above 69% of the 98 other Perplexity roles quoted over the same currency and period.
Perplexity concentrates this hiring in:
Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.
What the role asks for
What you'd do
- 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.
What they're asking for
- 5+ years (Senior) or 8+ years (Staff) of software engineering experience.Experience
- Strong experience building production data infrastructure systems.Skill
- Hands-on experience with batch and/or streaming data processing at scale.Skill
- Deep familiarity with data orchestration systems (Airflow, Dagster, or similar).Skill
- Proficiency in Python and at least one additional backend language (Go, TypeScript, etc.).Skill
- Strong systems thinking around reliability, latency, cost, and complexity tradeoffs.Skill
- Experience supporting ML/AI workflows, training pipelines, or evaluation systems.Skill
- Familiarity with data quality, lineage, observability, and governance tooling.Skill
- Prior ownership of internal platforms used by many teams.Skill
Parsed by MeritLog from the employer’s own posting. The full description follows below.
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 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. QUALIFICATIONS - 5+ years (Senior) or 8+ years (Staff) of software engineering experience. - Strong experience building production data infrastructure systems. - Hands-on experience with batch and/or streaming data processing at scale. - Deep familiarity with data orchestration systems (Airflow, Dagster, or similar). - Proficiency in Python and at least one additional backend language (Go, TypeScript, etc.). - Strong systems thinking around reliability, latency, cost, and complexity tradeoffs. - Experience supporting ML/AI workflows, training pipelines, or evaluation systems. - Familiarity with data quality, lineage, observability, and governance tooling. - Prior ownership of internal platforms used by many teams. If you’re excited about this role, we encourage you to apply even if your experience doesn’t match every qualification listed above.
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