Distinguished Engineer - Data Architect
at Sonicwall
Pune, Maharashtra, India, hybrid, executive, 12+ years
posted 21 Jul 2026
- Azure
- Apache Flink
- ClickHouse
- Qdrant
- Kafka
- Pinecone
- GCP
- Iceberg
- Delta Lake
- Pulsar
- Spark
- Milvus
- AWS
- DuckDB
- Kubernetes
About the role
Posted: 2026-07-21
For more than 30 years, SonicWall has delivered a partner-first unified cybersecurity portfolio that helps SMBs, MSPs, and IT teams consolidate network security, endpoint security, security service edge (SSE), and threat response across hybrid environments. By bringing connected technology, real-time threat intelligence, automation, and expert services together, SonicWall helps organizations simplify security operations, achieve better security outcomes, and lower total cost of ownership. Backed by a global ecosystem of more than 17,000 partners, SonicWall delivers enterprise-grade protection built for SMB and mid-market realities.
For more information, visit www.sonicwall.com or follow us on X, LinkedIn, Facebook, and Instagram.
Job Title: Distinguished Engineer - Data Architect
We are looking for a Distinguished Engineer- Data Architect to design and build the data foundation for our AI-driven security platform. In this role, you will own the architectural blueprint and core implementation of our data strategy, transforming massive streams of high-velocity network traffic logs, customer configurations, and security alerts into high-performance, ML-ready data fabrics.
As part of a lean, elite three-person Data & AI team, you will serve as the ultimate technical authority on data scale, streaming architecture, multi-tenant security, and cost-efficient storage systems.
What you would do
- Architect the Core Data Platform: Design, implement, and evolve a unified blueprint for streaming and batch data processing capable of ingesting, normalizing, and storing petabyte-scale, high-velocity security telemetry.
- Enable AI & Advanced Analytics: Partner directly with the Lead AI Engineer to architect the underlying storage, retrieval systems, and feature stores—including optimal vector database structures and data lakehouse layers—required for low-latency AI inference and training.
- Enforce Zero-Trust Data Governance: Architect strict multi-tenant isolation, data privacy guardrails, and compliance controls directly into the data fabric to protect sensitive customer network configurations and logs.
- Optimize Economics & Performance: Drive infrastructure cost-efficiency and performance tuning across data processing engines, storage layers, and cloud infrastructure, balancing ultra-low latency requirements with predictable cost scaling.
Read from job-boards.greenhouse.io, last seen 3d ago.