Staff Software Engineer, Data Platform

About the Role

Enterprises of all sizes trust Abnormal Security’s cloud products to stop cybercrime—and those products depend on data: reliable, scalable, and secure access to it. The Data Platform team builds and operates the core storage, streaming, and processing systems that power Abnormal’s AI-driven detection and prevention. Our mission is to provide robust, self-service data platforms that enable engineering and data science teams to innovate quickly, confidently, and at scale.

We’re looking for a Staff Software Engineer to drive the next generation of Abnormal’s data platform. In this role, you’ll set technical direction, lead ambitious cross-team initiatives, and mentor engineers while shaping how data flows and scales across our systems.

Staff Engineers are expected to be technical leaders at Abnormal. The ideal candidate:

• Tackles complex, ambiguous problems and turns them into actionable plans.
• Leads by example and dives deep when needed.
• Embodies our VOICE values and builds platforms as product offerings that delight users.
• Earns trust with our stakeholders across Engineering, Data Science, Analytics and Product through thoughtful collaboration.

What you will do 

• Define and drive the architecture and roadmap for Abnormal’s Data Platform, spanning storage, streaming, batch processing, and data infrastructure.
• Partner with engineers and data scientists to make pragmatic trade-offs, enabling a platform-first operating model and self-service data capabilities.
• Lead high-leverage technical initiatives such as scaling data systems across tenants and regions, improving resilience, and evolving our next-gen storage layer.
• Act as the technical lead for the team: shape quarterly plans, de-risk delivery, mentor engineers, and land impactful cross-org initiatives.
• Champion operational excellence across SLOs, availability, performance, incident response, and cost efficiency.
• Advocate for platform-as-a-product practices: crisp APIs, clear SLAs/SLOs, great docs, telemetry by default, and paved paths for developers.
• Guide Abnormal’s AI-native data workflows: data pipelines, feature storage, offline/online consistency, model evaluation, and data governance.

Must Haves 

• Proven experience building and scaling data-intensive, distributed systems in high-growth environments.
• 5+ years as a Senior+/Staff engineer building data platforms, infrastructure, or tools that materially increase engineering velocity and reliability.
• Depth in at least two of the following:
• Streaming systems (e.g., Kafka, Kinesis, SQS)
• Batch processing systems (e.g., Spark, Databricks, Airflow, DBT)
• Storage systems (e.g., PostgreSQL, MySQL, DynamoDB, RocksDB, Redis, OpenSearch, S3)
• Hands-on with our stack (or equivalent): Python, Golang, AWS, Databricks, Spark, Airflow, Kafka, Redis, RocksDB, PostgreSQL, Elasticsearch, Terraform, Kubernetes, etc.
• Strong fundamentals in distributed systems, observability, and reliability engineering (SLOs, incident management, capacity planning).
• A strong track record as a change agent, reshaping data platform strategy and delivering impactful, self-service offerings.
• Excellent ability and strong desire to onboard and mentor other engineers.

Nice to Haves 

• Experience with multi-tenant, multi-region or regulated platforms, including isolation, data governance, and guardrails.
• Prior leadership of cross-org migrations (e.g., to event-driven architectures or unified data platforms).

How We Work

• Product mindset: we treat our data platform as a product with clear APIs, docs, SLAs, and adoption metrics.
• Automation first: paved paths and golden configs over bespoke solutions.
• Measured outcomes: reliability, latency, cost, and developer experience drive our success.

#LI-ML1