# Principal Software Architect - Data Platform at Security Scorecard

- Company: Security Scorecard
- What the company does: Protect your supply chain and manage third-party cyber risk with SecurityScorecard. Trusted by 25,000+ organizations for continuous vendor monitoring and TPRM. Backed by Sequoia and GV.
- Company website: https://www.securityscorecard.com/
- Type: Startups
- Level: Principal and up
- Location: Hybrid (NYC / Austin)
- Work setup: Hybrid
- Pay: $270K to $330K base salary per year (USD)
- Posted: 2026-09-11
- Apply by: 2026-10-26
- Apply: https://job-boards.greenhouse.io/securityscorecard/jobs/8193353
- Page: https://www.1752.vc/careers/jobs/security-scorecard-principal-software-architect-data-platform/

## About the role

SecurityScorecard is hiring a Principal Software Architect to lead the system design of our data platform. Rating 12 million companies continuously means ingesting internet-scale measurement data, processing it across streaming, microbatch, and batch paths, storing it so it stays queryable and affordable as it grows, and serving analytics fast enough that customers can explore their own risk in real time. The data is not a byproduct of our product. It is the product.

## What they're looking for

- 10+ years of software or data engineering experience, including significant time architecting large-scale data platforms
- Deep expertise in stream and batch processing at scale with Kafka, Flink, and Spark or close equivalents, and clear judgment about which path a given workload belongs in
- Strong Python and PySpark, solid Java for Flink stream processing, and enough Scala to read and reason about an existing Spark codebase
- Hands-on experience designing lakehouse storage in production: columnar formats such as Parquet, open table formats such as Iceberg, and the partitioning, compaction, and schema evolution decisions that come with them
- Experience architecting OLAP and analytical serving layers (ClickHouse, Druid, Pinot, BigQuery, Snowflake or similar)
- Strong distributed systems fundamentals as they apply to data: exactly-once versus at-least-once semantics, ordering, backpressure, late and out-of-order data, and pipeline failure modes

Tags: Technology