Member of Technical Staff (Software Engineer, Data Platform)

Perplexity; Palo Alto (CA); On-site; USD 220,000 - 405,000; Full time

Job summary

Perplexity is seeking a Senior/Staff Data Platform Engineer to shape architecture across the data lifecycle, from ingestion to serving. You will drive standards and long-term direction for the data ecosystem, partnering with product engineering, data science, and analytics teams.

You will build scalable batch and streaming pipelines, lead orchestration with Airflow or Dagster, and ensure data quality, lineage, and observability at scale.

Qualifications

• 5+ years of software engineering experience (Senior) or 8+ years (Staff).
• Proven track record building production data infrastructure.
• Experience with batch and streaming data processing at scale.
• Hands-on with data orchestration systems (Airflow, Dagster).
• Proficiency in Python and at least one backend language (Go or TS).
• Experience supporting ML/AI workflows and governance tooling.
• Ownership of internal platforms used by many teams.

Responsibilities

• Design and operate large-scale batch and streaming data pipelines powering product features and AI workloads.
• Build real-time ingestion and batch processing systems (Kafka, Kinesis, PubSub, etc.) and backfills.
• Lead data orchestration architecture with Airflow or Dagster, managing SLAs and observability.
• Ensure data correctness, freshness, lineage, and recoverability amid evolving schemas.
• Create self-serve data platforms for engineers, data scientists, and analysts.
• Improve developer experience with abstractions, paved paths, and data-modeling standards.
• Drive architectural decisions across storage, compute, orchestration, and data APIs.
• Mentor engineers and raise the technical bar through reviews and collaboration.

Skills

Python

Go/TypeScript

Airflow/Dagster

Data pipelines

ML/AI workflows

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.

Compensation Range: $220K - $405K