The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.
Transform raw signals into actionable intelligence. Build the pipelines that turn chaos into clarity.
At detections.ai, we believe that great detection starts with great data. We're looking for a data engineer who doesn't just move bytes—but architects the intelligence infrastructure that helps security teams see threats before they strike. This is your chance to build the data foundation that powers the future of threat detection.
You’ll Own:
- Backend services end-to-end – from first design sketch to production deployment.
- High-throughput data pipelines that transform messy security telemetry into clean, enriched streams.
- Integrations with SIEMs, EDRs, and security platforms that make our product the connective tissue of the modern security stack.
- Cross-team collaboration with frontend, AI, and detection engineers to ensure data becomes truly actionable.
- Core architecture and APIs that other engineers (and customers) will build on every day.
You’re a Fit If You:
- Have 5+ years building and scaling backend systems that handle real-world complexity.
- Are fluent in Python (FastAPI preferred) and cloud-native development on AWS or GCP.
- Can design data models and transformation logic for complex, real-time workflows.
- Build with resilience, security, and performance as first principles.
- Thrive in a high-context, low-process environment where speed and ownership matter.
You’ll Stand Out If You:
- Have hands-on experience with SIEMs, SOARs, detection pipelines, or IR tools.
- Have worked with event streaming/orchestration systems (Kafka, Kinesis, Airflow).
- Understand security-first architecture (RBAC, audit logs, multi-tenancy).
- Have built on OpenSearch or DynamoDB for large-scale, fast data access.
Tech Stack:
- Frontend: React.js, Tailwind CSS, TypeScript
- Middle Layer: Node.js, TypeScript
- Backend: Python (FastAPI)
- Infra & DevOps: AWS, GCP, Docker, Terraform, GitHub Actions
- Data: OpenSearch, DynamoDB
- AI Agents: Gemini, Anthropic, OpenAI
- Tooling: Figma, Storybook, CI/CD pipelines
The Offer:
- Remote-first with meaningful equity
- Build for real users: Work side-by-side with detection engineers.
- Own meaningful problems: Architect the systems that make detections possible.
- Move fast without red tape: Small, high-context team with outsized impact.
- Shape product and culture: Your work is foundational to the company’s success.
This isn't just another data engineering role—it's your chance to build the data foundation that powers the next generation of threat detection.
Similar Jobs
Artificial Intelligence • Insurance • Machine Learning • Software • Analytics
Build and maintain automated data quality testing for customer data pipelines. Responsibilities include developing dbt, Soda, and SQL checks; validating schemas, loads, sequencing, and downstream reconciliations; detecting anomalies; managing severity levels and customer overrides; reporting quality metrics; and partnering with engineering teams to embed testing into new pipelines. The role requires strong backend testing, SQL, Python, Airflow, and Databricks experience, along with excellent debugging and communication skills.
Top Skills:
Apache AirflowCi/CdCypressDatabricksDbtDelta LakeGitPlaywrightPythonSodaSpark SqlSQL
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.
Top Skills:
Apache AirflowAWSAzureDagsterData LakeData ModelingData PipelinesDatabricksDelta LakeETLGCPGitGitPrefectPysparkPythonRest ApisSparksqlSQL
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering



