Data Engineer- Activity Group

About Cyera

Come join the company building the security operating model for the age of AI. AI has changed how data is used — and security must change with it. Cyera's mission is to empower businesses to accelerate AI Adoption by defining a holistic approach to securing AI - from data to access to model. Instead of perimeter controls and static policies, Cyera provides a unified control plane that understands relationships between data, access, and behaviors across humans, systems, and AI. Backed by the world’s leading investors and working with a large and growing list of Fortune 1000 companies, we are looking for world-class talent to join us as we usher in the new era of data and AI security.

Requirements:

About the Role

As a Data Engineer, you'll join the Activity Group — the team behind Access Trail, Cyera's activity monitoring product. Access Trail ingests and processes activity events from a wide range of enterprise data sources at very high throughput, turning billions of raw events into the trusted activity layer that powers investigations, analytics, and Cyera's AI-driven security capabilities. This is production infrastructure that enterprise customers depend on daily, operating at serious scale. You'll work across the full data pipeline — from ingestion and high-throughput Spark and ClickHouse ETLs through data modeling to the analytical layer serving the product — and help re-architect our processing layer as we push past current scale limits. You'll join a high-velocity group shipping weekly to a growing customer base.

What You'll Do

Partner closely with product and engineering stakeholders to translate strategic requirements into scalable data models and production-ready pipelines.
Architect and scale high-throughput, multi-stage ETL pipelines that ingest and process billions of activity events, designing incremental processing strategies that handle terabyte-scale datasets while balancing data quality, freshness, performance, and cost.
Build and optimize distributed processing workloads (Spark) and analytical workloads (ClickHouse), eliminating performance bottlenecks in production environments serving enterprise customers.
Own data modeling end to end — converting raw activity data from diverse sources into a trusted, well-tested, unified model that powers investigations, analytics, and product experiences.
Champion data quality and integrity: design validation, testing, and freshness monitoring that catch issues before customers do, and diagnose data quality incidents when they occur.
Monitor and optimize infrastructure spend across compute, storage, and orchestration, driving measurable efficiency improvements.

Must-Haves

4+ years of experience as a data engineer building and operating production data systems.
Strong proficiency in SQL, including complex transformations, window functions, and performance optimization in columnar/OLAP databases (ClickHouse a strong advantage).
Hands-on experience with Spark or similar distributed processing frameworks at scale.
Proven experience designing high-throughput ETL/ELT pipelines, including incremental processing patterns and throughput/cost tradeoffs.
Strong data modeling skills and a data-quality mindset — building tested, version-controlled transformations.
Proficiency in Python for pipeline development, tooling, and automation.
Strong communication skills — able to discuss technical tradeoffs with engineers and translate product needs into data models.

Preferred Qualifications

Production experience with ClickHouse or other columnar/OLAP systems.
Experience with streaming and messaging systems (Kafka or similar).
Experience with AWS data services (S3, EMR/Databricks, MWAA, ECS).
Familiarity with CDC/data replication tools (Debezium or similar).
Experience building integrations/connectors with third-party APIs and event sources.
Experience with multi-tenant data architectures or data access controls.
Exposure to data security, privacy, or compliance domains.