Member of Technical Staff (Software Engineer, Data Platform)

B Capital; Palo Alto (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Pantera Capital is looking for a Senior/Staff Data Engineer to join their Data Platform team in Palo Alto, California. This role focuses on designing and operating large-scale data pipelines and building self-serve platforms for teams while ensuring data correctness and reliability.

The ideal candidate will have over 5 years of software engineering experience with a strong focus on data infrastructure systems and orchestration tools like Airflow or Dagster. Proficiency in Python and additional backend languages is required.

Qualifications

• 5+ years of software engineering experience required for Senior role.
• 8+ years of experience for Staff role.
• Strong experience building production data infrastructure systems.
• Deep familiarity with data orchestration systems like Airflow or Dagster.
• Proficiency in Python and one additional backend language.

Responsibilities

• Design and operate large-scale batch and streaming data pipelines.
• Build event-driven and streaming systems for real-time data.
• Lead architecture of data orchestration using Airflow or Dagster.
• Set guarantees for data correctness and reliability.
• Build self-serve data platforms for engineers and data scientists.
• Drive architectural decisions across data APIs and storage.
• Mentor engineers and improve data infrastructure.

Skills

Software engineering experience

Production data infrastructure systems

Batch and streaming data processing

Data orchestration systems

Python programming

Systems thinking

Support for ML/AI workflows

Data quality and governance tools

Tools

Airflow

Dagster

Kafka

Databricks

Snowflake

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.