The Role
Lead the design of data platforms and pipelines, mentor engineers, and solve complex data challenges in a fast-paced environment.
Summary Generated by Built In
At SAFE Security, our mission is bold and ambitious: We Will Build CyberAGI — a super-specialized system of intelligence that autonomously predicts, detects, and remediates threats. This isn’t just a vision—it’s the future we’re building every day, with the best minds in AI, cybersecurity, and risk. At SAFE, we empower individuals and teams with the freedom and responsibility to align their goals, ensuring we all move towards this goal together.
We operate with radical transparency, autonomy, and accountability—there’s no room for brilliant jerks. We embrace a culture-first approach, offering an unlimited vacation policy, a high-trust work environment, and a commitment to continuous learning. For us, Culture is Our Strategy—check out our Culture Memo to dive deeper into what makes SAFE unique.
We’re looking for a Staff Data Engineer who thrives on solving complex data challenges at scale. You’ll be the technical force multiplier, leading the design of data platforms, pipelines, and lakehouse architectures that fuel AI-driven cyber risk quantification globally. If you’ve been waiting for a role where you can set data strategy, lead bold ideas, and shape large-scale data ecosystems—this is it.
What You’ll Do:
- Be the Data Tech Leader: Mentor engineers, champion data engineering best practices, and raise the bar for technical excellence across the org.
- Architect at Scale: Design and lead petabyte-scale data ingestion, processing, and analytics platforms using Snowflake, Apache Spark, Iceberg, Parquet, and AWS-native services.
- Own the Data Flow: Build streaming and batch pipelines handling billions of events daily, orchestrated through Apache Airflow for reliability and fault tolerance.
- Set the Standards: Define frameworks for data modeling, schema evolution, partitioning strategies, and data quality/observability for analytics and AI workloads.
- Code Like a Pro: Stay hands-on, writing high-performance data processing jobs in Python, SQL, and Scala, and conducting deep-dive reviews when it matters most.
- Master the Lakehouse: Architect data lakes and warehouse solutions that balance cost, performance, and scalability, leveraging AWS S3 and Snowflake.
- Solve Complex Problems: Elegantly and efficiently debug and optimize long-running jobs, data skew, and high-volume ETL bottlenecks.
- Collaborate and influence: Work with the Product, AI/ML, and Platform teams to ensure that data solutions directly power real-time cyber risk analytics.
- Innovate Constantly: Evaluate and introduce emerging data technologies (e.g., Flink, Druid, Rockset) to keep SAFE at the forefront of data engineering innovation.
What We’re Looking For:
- 8+ years of experience in data engineering, with a proven track record of designing and scaling distributed data systems.
- Deep expertise in big data processing frameworks (Apache Spark, Flink) and workflow orchestration (Airflow).
- Strong hands-on experience with data warehousing (Snowflake) and data lakehouse architectures (Iceberg, Parquet).
- Proficiency in Python, SQL, Scala, Go/Nodejs with an ability to optimize large-scale ETL/ELT workloads.
- Expertise in real-time data ingestion pipelines using Kafka or Kinesis, handling billions of events daily.
- Experience operating in cloud-native environments (AWS) and leveraging services like S3, Lambda, ECS, Glue, and Athena.
- Strong understanding of data modeling, schema design, indexing, and query optimization for analytical workloads.
- Proven leadership in mentoring engineers, driving architectural decisions, and aligning data initiatives with product goals.
- Experience in streaming architectures, CDC pipelines, and data observability frameworks.
- Ability to navigate ambiguous problems, high-scale challenges, and lead teams toward innovative solutions.
- Proficient in deploying containerized applications (Docker, Kubernetes, ECS).
- Familiarity with using AI Coding assistants like Cursor, Claude Code, or GitHub Copilot
Preferred Qualifications:
- Exposure to CI/CD pipelines, automated testing, and infrastructure-as-code for data workflows.
- Familiarity with real-time analytics engines (Druid, Pinot, Rockset) or machine learning data pipelines.
- Contributions to open-source data projects or thought leadership in the data engineering community.
- Prior experience in cybersecurity, risk quantification, or other high-scale SaaS domain
If you’re passionate about cyber risk, thrive in a fast-paced environment, and want to be part of a team that’s redefining security—we want to hear from you! 🚀
Top Skills
Apache Airflow
Spark
Athena
AWS
Docker
Ecs
Glue
Go
Iceberg
Kafka
Kinesis
Kubernetes
Lambda
Node.js
Parquet
Python
S3
Scala
Snowflake
SQL
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The Company
What We Do
Safe Security is a pioneer in the “Cybersecurity and Digital Business Risk Quantification” (CRQ) space. It helps organizations measure and mitigate enterprise-wide cyber risk in real-time using it’s ML Enabled API-First SAFE Platform by aggregating automated signals across people, process and technology, both for 1st & 3rd Party to dynamically predict the breach likelihood (SAFE Score) & $$ Value at Risk of an organization
Headquartered in Palo Alto, Safe Security has over 200 customers worldwide including multiple Fortune 500 companies averaging an NPS of 73 in 2020.
Backed by John Chambers and senior executives from Softbank, Sequoia, PayPal, SAP, and McKinsey & Co., it was also one of the Top Contributors to the National Vulnerability Database(NVD) of the U.S. Government in 2019 and the ATT&CK MITRE Contributor in 2020.
The company, since 2018, has also been working with MIT in joint research for the development of their SAFE Scoring Algorithm. Safe Security has received several awards including the Morgan Stanley CTO Innovation Award.
