About Kaseya

Kaseya is the leading provider of AI-powered IT management and cybersecurity software, serving Managed Service Providers (MSPs) and internal IT organizations worldwide. Our comprehensive platform helps organizations efficiently manage, secure, and automate their IT environments, driving operational efficiency and long-term business success.

Backed by Insight Partners, a leading global software investor, Kaseya has experienced sustained double-digit growth and continues to expand its global footprint. Today, Kaseya supports customers in more than 20 countries and manages over 15 million endpoints worldwide.

Founded in 2000, Kaseya was built by builders - and we're still building. We look for people who create rather than wait, who see a hard problem and lean in, and who treat challenges as raw material. At Kaseya, everyone plays a role in shaping the future of IT: whether you're in engineering, product, sales, marketing, customer support, or operations, your work helps protect, defend, and optimize IT environments across the globe.

We're building teams that grow, perform, and make an impact. If you're driven by the itch to make things better - a product, a process, a career - you'll fit right in.

At Kaseya, we don't just raise the bar. We build it.

Job Spec - Senior Staff Data Engineer - Data Technical Lead

About the Role

We are looking for a Senior Staff Data Engineer to serve as the overall technical lead for our Data charter, defining and driving the architecture for large-scale, real-time and analytical data systems.

This is a senior individual-contributor leadership role for an engineer who can operate across the entire data platform, from ingestion and change data capture through stream processing, lakehouse storage, query and serving layers, governance, observability, and downstream AI/ML consumption.

You will establish the technical direction for how data moves through the organization, define engineering standards and architectural patterns, lead the most complex cross-team initiatives, and ensure our data platform can operate reliably at significant scale.

You will lead through technical vision, architecture, influence, design quality, hands-on engineering, and mentorship.

You will work across Data Engineering, Platform, Product, Analytics, AI/ML, and application engineering teams and will be expected to make architectural decisions that span organizational and system boundaries.

What You’ll Own

Data Platform Architecture & Technical Strategy

Serve as the technical authority and architectural lead for the Data charter, defining the target-state architecture and multi-year technical roadmap for the data platform.
Own architectural decisions spanning:

Sources → Ingestion → Streaming/CDC → Processing → Storage → Catalog/Governance → Query/Serving → AI/ML & Product Consumption

Define reference architectures, engineering standards, reusable patterns, and technology choices for real-time, near-real-time, and batch data systems.
Drive architectural simplification and platform standardization, reducing one-off pipelines and fragmented technology choices in favor of reusable platform capabilities.
Evaluate architectural trade-offs across latency, throughput, consistency, durability, availability, scalability, operational complexity, developer experience, and cost.
Lead architecture and design reviews for high-impact data initiatives and establish the technical bar for production data systems.
Identify architectural bottlenecks and systemic risks before they become production or scaling problems.
Define clear boundaries and contracts between data infrastructure, application services, analytics systems, AI/ML platforms, and downstream consumers.

Real-Time Data Ingestion & Event Architecture

Own and evolve a high-scale ingestion architecture capable of supporting transactional, operational, event, telemetry, and analytical workloads.

Key areas include:

Apache Kafka / Confluent and equivalent distributed event-streaming platforms
Kafka topic, partitioning, replication, retention, and consumer-group strategies