Software Engineer - Data Platform | NYC, Seattle, SF

Perplexity • Seattle, WA

8h

About The Position

Perplexity is looking for experienced Data Infrastructure Engineers to design, build, and scale the foundational data systems that power our product, AI research, analytics, and decision-making at scale. In this role, you will develop and own critical infrastructure for batch and streaming data processing, data orchestration, reliability, and developer experience across the data stack. You’ll work closely with engineering and data science teams to ensure data is accurate, timely, discoverable, and trustworthy, while enabling teams to move fast without sacrificing correctness or scale. This is a high-impact, senior/staff-level role where you will shape architecture, set standards, and drive long-term technical direction for Perplexity’s data ecosystem.

Requirements

• 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: you understand tradeoffs across reliability, latency, cost, and complexity.
• 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.

Responsibilities

• Design and operate large-scale batch and streaming data pipelines supporting product features, AI training/evaluation, analytics, and experimentation.
• Build and evolve event-driven and streaming systems (e.g., Kafka/Kinesis/PubSub-style architectures) for real-time ingestion, transformation, and delivery.
• Own batch processing frameworks for backfills, aggregations, and offline computation.
• Lead the design and operation of data orchestration systems (e.g., Airflow, Dagster, or equivalent), including scheduling, dependency management, retries, SLAs, and observability.
• Establish strong guarantees around data correctness, freshness, lineage, and recoverability.
• Design systems that handle scale, partial failure, and evolving schemas.
• Build self-serve data platforms that empower engineers, data scientists, and analysts to safely create and operate pipelines.
• Improve developer experience for data work through better abstractions, tooling, documentation, and paved paths.
• Set standards for data modeling, testing, validation, and deployment.
• Drive architectural decisions across data infrastructure for storage, compute, orchestration, and APIs.
• Partner closely with engineering and data science teams to align data systems with evolving requirements.
• Mentor engineers, review designs, and raise the technical bar across the organization.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 101-250 employees