Sr Staff. Software Engineer

Kaseya Careers

Job details

  • ONSITE
  • UNKNOWN
  • Toronto
  • Verified 2026-11-02
  • Source: Kaseya Careers public GREENHOUSE source

Original job description

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 
  • Event-driven architectures 
  • Event contracts and event lifecycle management 
  • Schema Registry and schema evolution 
  • Avro, Protobuf, JSON, and related serialization formats 
  • Dead-letter queues, replay, backfill, quarantine, and recovery patterns 
  • Cross-region and cross-cloud event distribution 
  • Idempotency and duplicate handling 
  • Delivery semantics including at-most-once, at-least-once, and exactly-once processing 

Establish patterns that enable teams to safely publish and consume data without creating tight coupling between producers and consumers. 

CDC & Operational Data Integration 

Define the architecture for reliable movement of transactional data into the broader data ecosystem. 

This includes: 

  • Change Data Capture (CDC) 
  • Debezium 
  • Kafka Connect and connector ecosystems 
  • Transactional databases / OLTP systems 
  • Transaction logs and database replication 
  • Outbox patterns 
  • Event sourcing where appropriate 
  • Snapshot + incremental synchronization 
  • Data reconciliation and recovery 

Establish standards for handling ordering, duplicate events, schema evolution, transaction boundaries, late-arriving data, replay, and failure recovery. 

Work closely with application and database engineering teams to ensure CDC architectures do not compromise transactional database performance or correctness. 

Stream & Distributed Data Processing 

Define the processing architecture for high-throughput and low-latency data transformation and enrichment. 

Provide deep technical leadership across technologies such as: 

  • Apache Flink 
  • Apache Spark / Spark Structured Streaming 
  • Kafka Streams 
  • SQL-based stream processing 
  • Stateful stream processing 
  • Event-time and processing-time semantics 
  • Watermarks and late-arriving events 
  • Windowing 
  • Stream joins 
  • State management and checkpointing 
  • Backpressure 
  • Distributed processing and recovery 

Determine when workloads should use streaming, micro-batch, or batch architectures rather than forcing all workloads into a single processing paradigm. 

Establish reusable frameworks and patterns for ETL/ELT, enrichment, aggregation, deduplication, correlation, and complex event processing. 

Lakehouse, Storage & Data Management 

Own the architecture for durable, scalable analytical data storage. 

Key technologies and concepts may include: 

  • Object storage such as Amazon S3 
  • Apache Iceberg 
  • Open table formats 
  • Data lake / lakehouse architectures 
  • Partitioning and clustering 
  • Compaction 
  • Metadata management 
  • Snapshot isolation 
  • Time travel 
  • Schema and partition evolution 
  • Small-file management 
  • Lifecycle and retention management 

Define strategies for balancing storage cost, query performance, ingestion throughput, and operational simplicity. 

Establish clear architectural patterns for moving data between operational systems, streaming platforms, lakehouse storage, analytical databases, and downstream serving systems. 

Catalog, Metadata, Governance & Data Contracts 

Establish platform-level standards for making data discoverable, understandable, governed, and trustworthy. 

Own architectural patterns around: 

  • Data catalogs 
  • Metadata management 
  • Schema Registry 
  • Data lineage 
  • Data contracts 
  • Dataset ownership 
  • Schema compatibility 
  • Data classification 
  • Data retention 
  • Access control 
  • Data quality 
  • Data lifecycle management 

Ensure that data producers and consumers have explicit contracts rather than relying on undocumented assumptions. 

Drive a data-as-a-product mindset where critical datasets have clear owners, schemas, SLAs/SLOs, documentation, lineage, quality expectations, and lifecycle policies. 

Querying, Search & Serving Architecture 

Define how different consumers access data based on latency, scale, and workload requirements. 

Evaluate and establish patterns across technologies such as: 

  • Lakehouse query engines 
  • Analytical databases 
  • OLAP systems such as ClickHouse 
  • Operational databases 
  • Materialized views 
  • APIs and data services 
  • Low-latency serving layers 

Create clear architectural guidance around when data belongs in a streaming platform, object store/lakehouse, OLAP engine, search index, transactional database, cache, or specialized serving system. 

Optimize architectures for the appropriate combination of: 

  • Sub-second operational access 
  • Interactive analytics 
  • Large-scale analytical queries 
  • Historical analysis 
  • Search 
  • AI/ML workloads 

Technical Leadership 

As the Senior Staff Data Engineer, your impact extends well beyond the systems you personally implement. 

You will: 

  • Serve as the technical lead across the broader Data Engineering charter. 
  • Establish architectural principles and engineering standards used across multiple teams. 
  • Lead architecture reviews and provide technical direction for complex, ambiguous initiatives. 
  • Write and review architecture documents, RFCs, ADRs, design proposals, and technical roadmaps. 
  • Challenge designs constructively and drive teams toward simpler, more scalable architectures. 
  • Identify systemic technical debt and develop pragmatic strategies for eliminating it. 
  • Mentor Staff, Senior, and other engineers and help raise the technical bar across the organization. 
  • Create reusable frameworks and paved roads that increase engineering velocity across teams. 
  • Partner with engineering leadership on technical strategy, organizational priorities, platform investments, and build-vs-buy decisions. 
  • Influence without authority across multiple engineering organizations. 
  • Remain hands-on enough to prototype architectures, debug complex distributed-system failures, review critical code, and lead implementation of foundational platform capabilities. 

Required Qualifications 

  • 10+ years of software engineering, backend engineering, data engineering, or distributed-systems experience, with significant experience operating at Staff, Senior Staff, Principal, or equivalent technical scope. 
  • Demonstrated experience serving as a technical lead or architect for large-scale data platforms spanning multiple teams and business-critical systems. 
  • Deep expertise in distributed systems and large-scale data architecture. 
  • Strong hands-on experience with Apache Kafka or equivalent distributed streaming platforms. 
  • Strong experience with stream-processing technologies such as Apache Flink, Spark Structured Streaming, Kafka Streams, or equivalent systems. 
  • Experience designing and operating CDC architectures, preferably using technologies such as Debezium and Kafka Connect. 
  • Strong understanding of transactional databases, transaction logs, consistency models, ordering, replication, and distributed-system failure modes. 
  • Experience designing modern data lake or lakehouse architectures, including object storage and open table formats such as Apache Iceberg, Delta Lake, or Hudi. 
  • Deep understanding of data modeling, schemas, schema evolution, data contracts, metadata, lineage, governance, and data quality. 
  • Experience designing systems supporting both operational and analytical workloads. 
  • Strong programming ability in one or more languages such as Java, Go, Rust, Python, Scala, or equivalent. 
  • Strong cloud architecture experience with AWS, Azure, GCP, or comparable cloud environments. 
  • Strong understanding of cloud networking, IAM, security, infrastructure as code, and cloud-native architectures. 
  • Demonstrated experience designing systems for high availability, fault tolerance, disaster recovery, and production operability. 
  • Strong understanding of performance engineering and the ability to reason quantitatively about throughput, latency, storage, compute, network utilization, and cost. 
  • Proven ability to lead architecture and technical strategy across organizational boundaries. 
  • Excellent written and verbal communication skills, including the ability to communicate complex architectural trade-offs to both deeply technical and executive audiences. 

 

Preferred Qualifications 

  • Experience operating Kafka/Confluent environments at substantial scale. 
  • Deep experience with Apache Flink and stateful stream processing. 
  • Experience with Apache Iceberg and modern lakehouse architectures. 
  • Experience with analytical/OLAP systems such as ClickHouse, Druid, Pinot, Snowflake, BigQuery, Redshift, or similar technologies. 
  • Experience with OpenSearch or Elasticsearch and large-scale indexing architectures. 
  • Experience building multi-region or multi-cloud data platforms. 
  • Experience with Kubernetes and cloud-native infrastructure. 
  • Strong knowledge of Terraform, GitOps, CI/CD, and infrastructure-as-code practices. 
  • Experience designing multi-tenant data platforms. 
  • Experience supporting AI/ML platforms, feature engineering, RAG, embeddings, vector search, or real-time inference workloads. 
  • Experience building internal developer platforms, reusable data frameworks, or self-service data infrastructure. 
  • Experience defining data governance, lineage, catalog, and data-contract standards at organizational scale. 

 

What Success Looks Like 

Within your first 6–12 months, you will have: 

  • Established a clear target-state architecture and technical roadmap for the Data charter. 
  • Created architectural standards for ingestion, CDC, streaming, processing, storage, querying, governance, and observability. 
  • Identified and begun eliminating the highest-risk scalability, reliability, and architectural bottlenecks. 
  • Improved the consistency and quality of technical decisions across multiple engineering teams. 
  • Established reusable platform capabilities that reduce the amount of bespoke data engineering required by individual teams. 
  • Improved reliability and observability of critical data flows through measurable SLOs and operational standards. 
  • Raised the technical bar through architecture reviews, mentoring, design guidance, and hands-on leadership. 
  • Created a platform foundation capable of supporting the organization’s next generation of real-time, analytical, and AI-driven products. 

The Engineer We’re Looking For 

The ideal candidate is equally comfortable drawing the architecture on a whiteboard, debating distributed-systems trade-offs, reviewing a Flink or Kafka design, debugging a production pipeline, writing an RFC, and aligning several engineering teams around a common technical direction. 

You think in systems rather than individual pipelines. 

You understand that choosing Kafka, Flink, Iceberg, or ClickHouse is less important than understanding why, where each technology belongs, what failure modes it introduces, and how the overall system behaves under scale and failure. 

You know when strong consistency matters, and when eventual consistency is the better engineering decision. 

You understand the difference between achieving exactly-once processing in a controlled subsystem and claiming end-to-end exactly-once semantics across a distributed architecture. 

You care about developer experience as much as infrastructure sophistication and prefer paved roads and reusable platform capabilities over one-off solutions. 

Most importantly, you combine deep technical expertise with the ability to create clarity across complex systems and lead engineers toward a coherent architecture. 

 

 

Additional information
Kaseya provides equal employment opportunity to all employees and applicants without regard to race, religion, age, ancestry, gender, sex, sexual orientation, national origin, citizenship status, physical or mental disability, veteran status, marital status, or any other characteristic protected by applicable law.

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