Principal Software Developer – Data Architect

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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Experience enabling secure interoperability patterns with customer systems and AI workflows,

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.

• Practical familiarity with AI platform integration concepts such as MCP, AWS Bedrock, AWS

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.

• Experience working with DevOps teams, CI/CD pipelines, infrastructure-as-code, and operational

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.

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.

• 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.

• Mentor and guide teams: Cultivate high-performing development teams, driving adoption of best

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.

• AI -ready data patterns and tooling: AWS Bedrock (including models such as Anthropic Claude),

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