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.
We're looking for a Senior Data Engineer to help build Cyera's next-generation data platform — the lakehouse foundation that will power data processing across the entire product. This is not a "write pipelines on top of someone else's platform" role, and it's not a pure infrastructure role either. It's both, deliberately.
You'll own the platform end to end: the infrastructure it runs on (Spark on Kubernetes, Apache Iceberg, AWS Glue, Airflow), the frameworks and tooling that let dozens of other engineers build on it without reinventing the wheel, and the design of the data pipelines themselves. Everything you build becomes leverage for the teams around you — your abstractions, base images, CI/CD flows, and operational patterns are what make the platform usable at scale.
You'll also own one of the hardest ongoing trade-offs in a high-scale data platform: balancing cost and performance. Compute sizing, storage layout, partitioning and compaction strategy, job scheduling — every decision has a price tag and a latency profile, and you'll be the one making those calls with data.
This role is ideal for an engineer who is equally comfortable debugging a Spark executor OOM on Kubernetes at 10am, designing a clean Python framework API at noon, and modeling the cost impact of a table layout change in the afternoon.
Design, deploy, and operate our Spark-on-Kubernetes compute platform, including autoscaling, resource tuning, and multi-tenancy considerations.
Own the lakehouse storage layer built on Apache Iceberg and AWS Glue catalog — table design, partitioning, compaction, schema evolution, and retention.
Build and operate orchestration on Airflow: DAG standards, deployment flows, environment promotion, and reliability.
Own production operations of the platform: monitoring, alerting, incident response, and continuous hardening.
Frameworks & Developer Enablement
Build the code frameworks, libraries, and templates that other engineers use to write pipelines — so that spinning up a new production-grade Spark job is measured in hours, not weeks.
Define and enforce standards for pipeline structure, testing, observability, and deployment across teams.
Own CI/CD for data workloads: image builds, artifact promotion, and GitOps-based delivery.
Act as a technical partner to product and research teams building on the platform — your customers are other engineers.
Data Pipelines & Architecture
Design and build scalable batch and streaming pipelines processing complex, high-volume datasets from diverse sources.
Lead large-scale backfills and migration initiatives, ensuring data consistency and integrity across evolving storage and compute platforms.
Design event-driven data flows over large-scale queue systems (Kafka) for reliable, efficient data movement.
Cost & Performance
Continuously balance cost against performance: right-size compute, tune queries and jobs, optimize storage layout and file sizes, and choose the correct engine for each workload.
Build cost visibility and attribution into the platform so trade-offs are made with data, not guesswork.
Requirements
5+ years of experience in software engineering, with meaningful time spent building and operating large-scale data platforms.
Strong hands-on experience with distributed processing engines (Spark strongly preferred), including performance tuning and debugging in production.
Practical experience deploying and operating workloads in Kubernetes-based environments — you're not afraid of infra work; you enjoy it.
Experience building shared frameworks, libraries, or internal tooling used by other engineers, with the product mindset that comes with it (clean APIs, docs, versioning, backward compatibility).
Strong proficiency in SQL and data modeling: complex analytical queries, query tuning, partitioning strategies.
Experience with cloud-native data infrastructure on AWS (or equivalent) at high scale.
A strong sense of ownership — from design through deployment, operation, and cost.
Ability to thrive in a fast-changing environment, making pragmatic decisions with incomplete information.
Nice to have
Hands-on experience with Apache Iceberg or other open table formats (Delta Lake, Hudi) — compaction, schema evolution, catalog management.
Experience with EMR on EKS, Spark Operator, or similar Spark-on-Kubernetes setups.
Experience with Airflow at scale (custom operators, deferrable operators, multi-environment deployment).
Kafka-heavy or event-driven architectures; CDC pipelines (Debezium or similar).
GitOps tooling and infrastructure-as-code.
Experience with FinOps / cloud cost optimization for data workloads.
Background in cybersecurity-related data infrastructure or compliance-constrained environments (e.g., FedRAMP / GovCloud).
Contributions to open-source data engineering tools or frameworks.
Why Join Us?
At Cyera, we own what we build and how we work. Cyerans are empowered to take initiative, move quickly, and turn ideas into impact. We push boundaries by challenging the status quo, learning fast, and continuously raising the bar — for ourselves and for the industry. We elevate together by lifting each other up, celebrating wins as a team, and recognizing that our success is shared.
In this role specifically: everything you build becomes the foundation other engineers stand on. You'll work at the technical heart of the company's data platform, solve problems that only exist at real scale, and shape how an entire engineering organization works with data.
Feel free to apply even if your experience doesn't tick every box. We're building something special here — and we welcome Cyerans with diverse backgrounds, perspectives, and experiences.