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, SecurityScorecard’s 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. In 2023, SecurityScorecard was named to Fast Company’s list of the World’s Most Innovative Companies and to the Achievers 50 Most Engaged Workplaces award.

About the Role

SecurityScorecard is hiring a Principal Software Architect to lead the system design of its data platform. The platform must ingest internet‑scale measurement data, process it across streaming, micro‑batch, and batch paths, store it affordably and queryably as it grows, and serve analytics fast enough for real‑time customer risk exploration. Data quality is critical because the ratings directly affect underwriting and pricing decisions.

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 between producers and consumers, including schema ownership, compatibility rules, and handling of breaking changes.
• Design the lakehouse architecture: 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 for customers, ad‑hoc internal analytics/Data Science exploration, and bulk delivery to external feeds/partners—ensuring isolation so one workload never degrades another.
• 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 across engineering, providing substantive feedback on architecture and risk.
• Partner with AI and Front‑End Architects on data access patterns.
• 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 with Kafka, Flink, and 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 an existing Spark codebase.
• Hands‑on experience designing lakehouse storage in production: columnar formats such as Parquet, open table formats such as Iceberg, and associated partitioning, compaction, and schema‑evolution decisions.
• 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, and 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.
• Demonstrated influence without authority: presenting technical direction to skeptical engineers, earning genuine buy‑in, and providing rigorous design‑review feedback on systems you didn’t build.
• Strong technical writing and mentorship: TDRs, design docs, decision records, and a history of raising the technical bar.
• 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 observability data, or a cybersecurity industry background.
• Track record of reducing platform cost at scale through storage tiering, query governance, or compute right‑sizing.

Our Tech Stack

The platform runs on Node.js and TypeScript with a React micro‑frontend architecture and PostgreSQL and ClickHouse for storage. Kafka is used for event streaming; infrastructure runs on AWS with Kubernetes, Terraform, Helm, and ArgoCD. Stream processing runs on Flink in Java, while batch and micro‑batch run on Spark. Some high‑performance pipeline components are written in C++. The data side relies on Kafka as the backbone, Flink for streaming, Spark for batch, and ClickHouse for analytical workloads.

Benefits

Specific to each country, SecurityScorecard offers a competitive salary, stock options, health benefits, unlimited PTO, parental leave, tuition reimbursements, and more.

Compensation

The estimated total compensation range for this position is $240,000 – $300,000 (base plus bonus). Actual compensation is based on factors such as affordability, skills, qualifications, and experience, and may vary from the range. In addition to base salary, employees may be eligible for annual performance‑based incentive awards and equity.