Member of Technical Staff (Software Engineer, Data Platform)

Perplexity; California (MO); On-site; USD 180,000 - 240,000; Full time

Job summary

Perplexity is seeking a senior/staff data platform architect to shape the long-term direction of our data ecosystem. You will own end-to-end data pipelines, drive orchestration strategy, and help teams safely discover and deploy data products.

You will mentor engineers, collaborate with product and data science, and push for strong observability and data quality. This role emphasizes scale, reliability, and ML/AI workflow support.

Qualifications

• 5+ years of software engineering for senior, 8+ for staff level.
• Proven track record building production data infra at scale.
• Experience with batch and streaming data processing.
• Strong knowledge of data orchestration tooling (Airflow/Dagster).

Responsibilities

• Design and operate large-scale batch and streaming pipelines powering product features, analytics, and AI workflows.
• Lead architecture of data orchestration and ensure end-to-end observability and reliability.
• Set standards for data correctness, freshness, lineage, and recoverability.
• Build self-serve data platforms enabling engineers and scientists to define pipelines safely.
• Mentor engineers, review designs, and raise the technical bar for data infrastructure.

Skills

Data platform design

Distributed systems

Python

Go/TypeScript

Data modeling

Observability

ML/AI workflows

Tools

Airflow

Dagster

Kafka

Kinesis

Pub/Sub

Databricks

Snowflake

Spark

dbt

Iceberg

Delta Lake

ClickHouse

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.