Lead design and delivery of an AI-ready, AWS-native enterprise data platform and lakehouse. Define architecture patterns, modeling standards, ingestion/CDC, observability, governance, and secure interoperability. Mentor teams, contribute hands-on to platform modernization, enable vector/RAG AI workflows, and provide reference implementations to drive consistent, scalable data products and analytics.
Caseware is one of Canada's original Fintech companies, having led the global audit and accounting software industry for over 30 years, with more than 500,000 users across 130 countries and available in 16 different languages. While you might not have heard of us (yet) over 36,000 accounting and audit professionals list Caseware as a skill on their LinkedIn profiles!
We are seeking a Principal Software Developer – Data Architect to drive the technical vision and architectural
strategy of Caseware’s enterprise data platform, including the AI-Ready Data Platform. This role will define
the enterprise data architecture, patterns, and modeling standards that deliver trusted, governed, high-quality
data products forming a foundational data platform for our cloud offerings, enabling AI capabilities and secure
interoperability with customer systems, while powering analytics and strengthening our core products.
This role requires deep experience designing modern data platforms and practical familiarity with how data
supports AI workflows, including retrieval, search, grounding, and secure interoperability patterns. You will
apply this experience to build a data foundation that supports AI workflows and agentic capabilities, analytics,
and customer interoperability.
This is a key leadership role where you will act as a hands-on architect while mentoring the development team,
guiding the long-term technical vision, shaping enterprise data architecture standards across teams, and
contributing to crucial AI and data platform projects.
❗ This is a full-time permanent position
❗ This is a new vacancy
📍 Location: This is a hybrid role requiring the successful candidate to work 3 days a week in our Toronto office located at 351 King St E Suite 1100 Toronto ON.
What you will be doing:
• Lead enterprise data platform architecture and modernization: Define and execute the technical
strategy for a scalable, AI-Ready, enterprise data platform, including Sherlock modernization,
lakehouse architecture, data products, interoperability, and the patterns and capabilities needed to
support AI-Ready use cases.
strategy for a scalable, AI-Ready, enterprise data platform, including Sherlock modernization,
lakehouse architecture, data products, interoperability, and the patterns and capabilities needed to
support AI-Ready use cases.
• Establish data architecture patterns: Create and evolve reference architectures, modeling
standards, guardrails, and best practices for our foundational data platform, including Icebergbased lakehouse architecture, medallion patterns, ingestion, normalization, data quality, and
interoperability.
standards, guardrails, and best practices for our foundational data platform, including Icebergbased lakehouse architecture, medallion patterns, ingestion, normalization, data quality, and
interoperability.
• Use and mentor teams on AI-assisted workflows: Apply AI tools in daily architecture, analysis,
documentation, and prototyping, and mentor teams in responsible usage that improves design
quality, data discovery, and delivery effectiveness.
documentation, and prototyping, and mentor teams in responsible usage that improves design
quality, data discovery, and delivery effectiveness.
• Oversee key platform projects: Contribute heavily to AI-Ready data platform initiatives and crossproduct data architecture improvements, including data layer re-architecture for our SE and
Sherlock products, schema modernization, and data model evolution.
Sherlock products, schema modernization, and data model evolution.
• Mentor and lead: Guide teams in delivering projects, fostering a mentorship culture, and ensuring
adherence to high standards in data engineering practices, data modeling, data quality, and
platform architecture.
adherence to high standards in data engineering practices, data modeling, data quality, and
platform architecture.
• Drive best practices: Collaborate with R&D groups to implement best practices for making trusted,
AI-Ready, and securely interoperable data proucts, including data contracts, ingestion and
normalization standards, and improving consistency and reuse across products
AI-Ready, and securely interoperable data proucts, including data contracts, ingestion and
normalization standards, and improving consistency and reuse across products
• Partner on data governance and security: Work with Security and product teams to define data
classification, retention, tenant isolation, and access controls for datasets and data products.
classification, retention, tenant isolation, and access controls for datasets and data products.
• Enable adoption through paved roads: Provide reference implementations and blueprints that
make it easy for teams to produce data products and integrate with the data platform.
make it easy for teams to produce data products and integrate with the data platform.
• Architect for data observability: Define and implement standards for data quality, lineage and
traceability, data dictionary controls, freshness monitoring, and alerting, so data products are
reliable and audit-ready.
traceability, data dictionary controls, freshness monitoring, and alerting, so data products are
reliable and audit-ready.
What you will bring:
• 10+ years of experience in software development and data engineering, with at least 5 years in a senior
technical leadership role, preferably as a Principal Developer or Data Architect.
technical leadership role, preferably as a Principal Developer or Data Architect.
• Deep experience designing modern data platforms on AWS cloud-native infrastructure, including
lakehouse, medallion, and analytics patterns, ingestion from OLTP systems, ETL/ELT pipelines,
distributed processing with Spark, Trino, and delivering analytics and AI-Ready data lakes at scale, with
strong operational practices.
lakehouse, medallion, and analytics patterns, ingestion from OLTP systems, ETL/ELT pipelines,
distributed processing with Spark, Trino, and delivering analytics and AI-Ready data lakes at scale, with
strong operational practices.
• Practical, hands-on use of AI tools to improve data architecture and engineering workflows, including
analysis, design exploration, documentation, prototyping, code assistance, and mentoring teams on
responsible, effective usage.
analysis, design exploration, documentation, prototyping, code assistance, and mentoring teams on
responsible, effective usage.
• Hands-on experience with core data technologies and integration patterns: MongoDB, Amazon
DocumentDB, MS SQL Server, DynamoDB, AWS ElastiCache for Redis, and Valkey; event streaming and
queueing using SNS/SQS. Postgres, pgvector, and Kafka or Pub/Sub are an asset.
DocumentDB, MS SQL Server, DynamoDB, AWS ElastiCache for Redis, and Valkey; event streaming and
queueing using SNS/SQS. Postgres, pgvector, and Kafka or Pub/Sub are an asset.
• Hands-on experience with AWS data platform services: S3, S3 Express, Athena, Glue Catalog, Lake
Formation, OpenSearch Serverless, S3 Vector Storage, Iceberg, Lambda, Step Functions, EKS, ETL on
EMR, and EMR Serverless.
Formation, OpenSearch Serverless, S3 Vector Storage, Iceberg, Lambda, Step Functions, EKS, ETL on
EMR, and EMR Serverless.
• Proven ability to architect and deliver scalable, reliable data systems and product data architectures,
guiding teams in data models, storage and integration architectures, data contracts, data domain
taxonomy, schema and event versioning, and resolving performance and scale bottlenecks.
guiding teams in data models, storage and integration architectures, data contracts, data domain
taxonomy, schema and event versioning, and resolving performance and scale bottlenecks.
• Proficiency in data movement and performance architecture: Experience designing replication, event
sourcing, and CDC/change tracking strategies, safe historical reprocessing patterns, and performance
optimization through query analysis, indexing, and partitioning.
sourcing, and CDC/change tracking strategies, safe historical reprocessing patterns, and performance
optimization through query analysis, indexing, and partitioning.
• Experience defining data governance and platform adoption standards in large organizations, including
controls for privacy, access, auditability, safe reuse, and operational guardrails for AI-Ready datasets
and data products.
controls for privacy, access, auditability, safe reuse, and operational guardrails for AI-Ready datasets
and data products.
• Experience enabling secure interoperability patterns with customer systems and AI workflows,
including governed data access, tenant-aware controls, and safe integration patterns.
including governed data access, tenant-aware controls, and safe integration patterns.
• Familiarity wth AI-ready data patterns is preferred, including embedding pipelines, vector-based
retrieval, RAG data workflows, and real-time/event-driven data flows that support AI integrations.
retrieval, RAG data workflows, and real-time/event-driven data flows that support AI integrations.
• Practical familiarity with AI platform integration concepts such as MCP, AWS Bedrock, AWS
Knowledge Bases, vector retrieval, and RAG workflows is preferred.
Knowledge Bases, vector retrieval, and RAG workflows is preferred.
• Strong technical leadership: Experience mentoring teams, setting engineering and architecture
standards, and influencing technical direction across multiple teams.
standards, and influencing technical direction across multiple teams.
• Experience working with DevOps teams, CI/CD pipelines, infrastructure-as-code, and operational
tooling to deliver scalable, resilient data platforms and pipelines.
tooling to deliver scalable, resilient data platforms and pipelines.
• Communication and collaboration skills to align cross-functional teams and engage with senior
leadership on technical strategy, trade-offs, and decisions.
leadership on technical strategy, trade-offs, and decisions.
Key Success Factors:
• Establish a solid technical strategy: Collaborate with data platform, product, and architecture
leadership to define the AI-Ready Data Platform’s technical direction, ensuring alignment with
business growth, scalability, and interoperability objectives.
• Deliver architecture patterns and standards: Define, prototype, and socialize key data architecture
patterns and modeling standards backed by reference documentation and architecture decision
records that teams can apply consistently.
patterns and modeling standards backed by reference documentation and architecture decision
records that teams can apply consistently.
• Advance key platform initiatives: Contribute significantly to AI-Ready Data Platform initiatives
and cross-product data architecture improvements, strengthening the foundation for AI
capabilities, interoperability, scalability, and performance.
and cross-product data architecture improvements, strengthening the foundation for AI
capabilities, interoperability, scalability, and performance.
• Mentor and guide teams: Cultivate high-performing development teams, driving adoption of best
practices in data modeling, data quality, governance, and operational excellence.
practices in data modeling, data quality, governance, and operational excellence.
Technologies you’ll work with:
• Core (current): AWS S3, S3 Express, DynamoDB, Athena, Glue Catalog, Lake Formation,
OpenSearch Serverless, S3 Vector Storage, EMR/EMR Serverless, Spark, Trino, MapReduce,
Iceberg, Lambda, Step Functions, EKS, SNS/SQS; MongoDB, Amazon DocumentDB, MS SQL
Server, Redis/Valkey; Java (Spring), Python.
OpenSearch Serverless, S3 Vector Storage, EMR/EMR Serverless, Spark, Trino, MapReduce,
Iceberg, Lambda, Step Functions, EKS, SNS/SQS; MongoDB, Amazon DocumentDB, MS SQL
Server, Redis/Valkey; Java (Spring), Python.
• AI -ready data patterns and tooling: AWS Bedrock (including models such as Anthropic Claude),
AWS Knowledge Bases, MCP, embeddings, vector retrieval, and RAG.
AWS Knowledge Bases, MCP, embeddings, vector retrieval, and RAG.
• Observability & operations: CloudWatch, New Relic, OpenTelemetry.
• Emerging: Kafka or Pub/Sub, LLM proxy layer (e.g. LLMProxy), Aurora PostgreSQL, pgvector
What's in it for you:
▪️Innovation is at our core. We work with cutting-edge technology in accounting and financial reporting, constantly pushing the boundaries to create impactful software solutions.
▪️We are committed to a collaborative culture, where your ideas are valued, and knowledge sharing is encouraged within a supportive, inclusive team.
▪️Work-life balance is important to us. We offer flexible work options, remote opportunities, and generous time-off policies to ensure a healthy work-life balance.
▪️We offer competitive compensation, including a competitive salary and comprehensive benefits such as health insurance and retirement plans.
▪️We are driven by impactful work. Your contributions directly affect how our clients manage financial processes and drive their success.
▪️Recognition and rewards matter to us. We celebrate hard work through recognition programs, performance bonuses, and opportunities for career growth.
▪️We embrace global opportunities. Work on international projects and collaborate with a diverse, global team.
About Caseware:
Caseware's cutting-edge software products are meticulously designed for accounting firms, corporations, and governments. Our teams are continually collaborating, innovating, and building upon our existing suite of products. With a customer-focused mindset, we are building technology that is shaping what the future of audits, financial reporting, and financial data analytics will look like.
With a recent strategic investment from Hg Capital in 2020, Caseware is now in its next major growth phase as we double down on the people and products that have made Caseware so successful to date.
One of Caseware's core values is Many Voices, One Team and with that in mind, we're dedicated to building teams as diverse as our customers in an equitable and inclusive way. We welcome and encourage candidates of all backgrounds to apply. Should you require accommodations or have any questions at any point during the application or interview process, please e-mail our People Operations team at [email protected].
AI Usage:
The recruitment process may use AI assisted tools but not for candidate screening or assessment. All final hiring decisions are made by humans to ensure fairness, transparency, and oversight.
Background Check:
Any candidates successful in obtaining an offer for a position will need to successfully complete a background check through Certn.co which typically includes an Identity Verification and Criminal Record Check. Executives and Senior Managers will undergo a Soft Credit Check as well. Candidates residing in the Netherlands and Germany are excluded from undergoing background checks via Certn.co
Security and Fraud:
Caseware takes the security of candidates seriously. All legitimate communication from us will come from email addresses ending in @caseware.com and our open positions are always listed on reputable job boards and on our website https://jobs.lever.co/caseware. We will NEVER ask for payment or financial information from you. If you receive an unsolicited job offer, proceed with extreme caution.
Caseware Toronto, Ontario, CAN Office
351 King St East, Suite 1100, Toronto, ON, Canada, M5V 1J6
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