Senior Backend Software Engineer – Data Platform

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 has built a culture centered around innovation, accountability, and results. We are a high-growth, high-performance organization that values individuals who are driven, adaptable, and committed to delivering exceptional outcomes for our customers and teammates alike.

At Kaseya, success comes from embracing challenges, moving with urgency, and continuously raising the bar.

Senior Software Engineer – Data Platform

Location: Toronto, Canada

Why Kaseya?

Kaseya powers millions of endpoints and serves thousands of customers worldwide. We are building a new Data & AI Platform that will unify data across Kaseya products and enable new customer experiences, automation, analytics, and AI capabilities.

We are looking for strong backend software engineers who want to solve large-scale data problems .

This is a software engineering role within the Data & AI Platform organization . You will design and build distributed backend services and platform infrastructure responsible for ingesting, processing, serving, and exposing data across Kaseya products.

This is not a traditional ETL, BI, analytics engineering, or data warehousing role.

Strong candidates typically come from backend engineering, distributed systems, infrastructure, streaming, or software-oriented data platform backgrounds.

What You'll Build

You will work on greenfield platform capabilities that sit across Kaseya's product ecosystem.

You will:

Design and build backend services, APIs, and distributed platform components used by internal engineering teams and customer-facing products

Build highly available services capable of processing and serving large volumes of data

Design event-driven and asynchronous systems using technologies such as Kafka and other distributed messaging platforms

Develop services responsible for data ingestion, enrichment, routing, transformation, and delivery

Build APIs and service interfaces that expose platform capabilities to other engineering teams

Design systems that operate correctly under concurrency, partial failures, retries, duplicate events, and high load

Solve distributed-systems problems involving:

partitioning

ordering

idempotency

retries

state management

backpressure

consistency

fault tolerance

delivery guarantees

Build reusable frameworks and platform primitives that can be adopted across multiple Kaseya products

Design service contracts, APIs, schemas, and integration patterns between independently developed systems

Improve platform performance, scalability, reliability, and operational efficiency

Build production observability using metrics, logging, tracing, dashboards, and alerting

Write automated unit, integration, and system tests

Own services throughout their lifecycle, including design, implementation, deployment, production support, incident response, and optimization

Participate in architecture reviews and technical design discussions

Collaborate closely with Product, Infrastructure, Security, AI, and other Software Engineering teams

Mentor engineers and help establish strong software engineering standards

What We're Looking For

Required

6+ years of professional software engineering experience

Significant recent hands-on experience building backend, platform, or distributed systems

Strong programming ability in one or more general-purpose languages such as:

Java

Go

Python

Scala

Rust

C++

Experience designing and developing production APIs, microservices, backend services, or platform services

Strong understanding of software engineering fundamentals, including:

data structures and algorithms

concurrency

asynchronous programming

API design

testing

debugging

system design

Experience designing or operating distributed systems in production

Experience with event-driven architectures or distributed messaging systems such as:

Kafka

Kinesis

Event Hubs

Pub/Sub

RabbitMQ

similar technologies

Experience building systems that handle failures, retries, duplicate processing, ordering, and distributed state

Experience operating cloud-native production systems on AWS, Azure, or GCP

Experience owning production services, including troubleshooting, monitoring, incident response, and reliability improvements

Experience working with Docker and containerized production environments

Strongly Preferred

Deep production experience with Apache Kafka

Experience with streaming frameworks such as:

Apache Flink

Spark Structured Streaming

Kafka Streams

Apache Beam

Experience designing high-throughput or low-latency systems

Experience with CDC, event sourcing, asynchronous processing, or message-driven architectures

Experience designing partitioning and sharding strategies

Understanding of message delivery semantics such as at-most-once, at-least-once, and effectively/exactly-once processing

Experience implementing idempotent and fault-tolerant services

Experience with Kubernetes and production container orchestration

Infrastructure-as-code experience with Terraform, Pulumi, CloudFormation, or similar tooling

CI/CD and deployment automation experience

Experience with distributed caching, queues, key-value stores, or high-performance databases

Experience designing developer-facing platforms or shared infrastructure used by multiple engineering teams

Strong observability and SRE practices using technologies such as Prometheus, Grafana, Datadog, OpenTelemetry, or similar systems

Experience conducting architecture reviews or authoring technical design documents, RFCs, or ADRs

Data Platform Experience

You do not need to come from a traditional Data Engineering background.

However, you should be comfortable working with data-intensive systems.

Relevant experience may include:

Real-time data ingestion

Streaming platforms

Distributed processing

Change Data Capture

Data-serving infrastructure

Large-scale analytical systems

Data APIs

Lakehouse infrastructure

Distributed storage systems

High-volume event processing

Experience with technologies such as the following is useful, but not a substitute for strong software engineering fundamentals :

Spark

Databricks

Snowflake

Iceberg

Delta Lake

BigQuery

Redshift

Candidates whose experience is primarily focused on ETL development, BI/reporting, dimensional modeling, dashboards, or data warehouse implementation without substantial backend software engineering experience are unlikely to be a fit for this role.

Bonus

Experience building infrastructure supporting machine learning or AI systems

Experience building AI/LLM backend services

Experience with vector databases or high-scale retrieval systems

Experience with RAG infrastructure or AI data pipelines