Principal Software Architect - Data Platform

About SecurityScorecard

SecurityScorecard is the global leader in cybersecurity ratings, with over 12 million companies continuously rated, operating in 64 countries. Founded in 2013 by security and risk experts Dr. Alex Yampolskiy and Sam Kassoumeh and funded by world‑class investors, its patented rating technology is used by over 25 000 organizations for self‑monitoring, third‑party risk management, board reporting, and cyber‑insurance underwriting. Headquartered in New York City, the company has been recognized by Inc Magazine as a “Best Workplace,” by Crain’s NY as a “Best Places to Work in NYC,” and as one of the 10 hottest SaaS startups in New York for two years in a row. It was named to Fast Company’s 2023 list of the World’s Most Innovative Companies and to the Achievers 50 Most Engaged Workplaces in 2023. Investors include Silver Lake Waterman, Moody’s, Sequoia Capital, GV and Riverwood Capital.

About the Role

SecurityScorecard is hiring a Principal Software Architect to lead the system design of its data platform. The platform ingests internet‑scale measurement data, processes it via streaming, micro‑batch, and batch paths, stores it affordably and queryably, and serves analytics in real time. Data quality is critical because the ratings directly affect underwriting and customer scores. This individual‑contributor role reports to the Chief Architect and works alongside a Principal Architect for AI and a Principal Front‑end Architect, partnering with engineering leadership, Product, and Data Science.

What You’ll Do

• Own the end‑to‑end system design for the data platform, from ingestion through the serving layer.
• Define service boundaries and data contracts, including schema ownership, compatibility rules, and handling of breaking changes.
• Design the lakehouse (table format, partitioning strategy, schema evolution, compaction, and metadata growth at scale).
• Architect the analytical serving layer for three workload classes—low‑latency high‑concurrency queries, ad‑hoc internal analytics, and bulk delivery to external feeds—ensuring isolation between them.
• Set direction on languages and frameworks in the data stack.
• Engineer data quality and observability into the platform (validation/quarantine paths, freshness and completeness SLOs, drift detection, and automated lineage/metadata).
• Design for correctness and reproducibility in the ratings pipeline, including backfills and historical restatements when scoring logic changes.
• Write Technical Design Reviews (TDRs), design docs, and standards that guide data architecture across teams, and embed that intent into repositories.
• Review TDRs from other engineering groups, providing substantive feedback on architecture and risk.
• Partner with AI and Front‑End Architects on data access patterns and mentor senior and staff engineers on data system design.

Required Qualifications

• 10+ years of software or data engineering experience, with significant time architecting large‑scale data platforms.
• Deep expertise in stream and batch processing at scale using Kafka, Flink, Spark, or close equivalents, and clear judgment about workload placement.
• Strong Python and PySpark skills; solid Java for Flink stream processing; enough Scala to read and reason about existing Spark codebases.
• Hands‑on experience designing lakehouse storage in production (Parquet, Iceberg, partitioning, compaction, schema evolution).
• 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 vs at‑least‑once semantics, ordering, backpressure, late/out‑of‑order data, pipeline failure modes).
• Track record of building data quality, contracts, and observability as engineered system properties (assertions, schema enforcement, lineage in code).
• Experience owning a large‑scale data migration while preserving history and correctness through cutover.
• Proven ability to influence without authority—presenting technical direction to skeptical engineers and earning genuine buy‑in, plus rigorous design‑review feedback on systems you didn’t build.
• Strong technical writing and mentorship: TDRs, design docs, decision records, and raising the technical bar around you.
• Comfort operating as a senior individual contributor, driving outcomes through prototyping and technical credibility.

Preferred Qualifications

• Experience building the data layer underneath ML or LLM systems (feature stores, vector stores, retrieval pipelines).
• Experience with internet‑scale scan, telemetry, or