Job Description & How to Apply Below
Position Summary Modular Solutions is seeking a Director of Data & Analytics to own the architecture and governance of our data and analytics platform and how our data is turned into decisions, so that the data we collect becomes reporting and answers that clients and teams can trust as we continue to grow our platform, client base, and internal capabilities.
Reporting to the CTO, this role leads the Data function, including data engineering, data science, business intelligence, analytics, and migration, and owns the architecture of the data and analytics platform that connects them. While the data and analytics platform is the primary focus, the role also contributes to and collaborates on the data architecture and design of our core application alongside our Product and Development teams.
The ideal candidate is a hands-on leader who sets direction and stays close to the work: designing the analytics data architecture, data models, and semantic layer alongside engineers rather than managing from a distance. You should bring deep experience in data architecture, data modelling, BI and analytics, and data governance, with a working knowledge of data migration and AI-enabled workflows, along with the ability to lead people, prioritize work, and translate complex technical concepts into clear business outcomes.
This role is well suited for someone who can build structure, develop teams, improve processes, and help Modular responsibly adopt data and AI practices that create real value for clients, employees, and the platform.
Key Responsibilities Set the data strategy and long-term roadmap for the Data function, including platforms, vendors, budget, and cost, and build a high-performing team across data engineering, BI and analytics, and data migration.
Own the data and analytics platform architecture: the target-state design for how data flows out of our application into ingestion and pipelines, the data warehouse and data marts, the semantic layer, and the BI and AI tools that consume them, supported by reference architectures and documented architecture decisions.
Define and maintain the analytics data model for insurance data, including policy, billing, claims, bordereaux, brokers, and coverage, aligned with the application's data model, so that migration, reporting, and analytics map to it consistently.
Collaborate with Product and Development on the data architecture and design of our core application: bring a data perspective to schema and data model design, assess the downstream impact of changes, favour incremental improvements over large-scale redesign, and establish data contracts so that application changes do not break reporting, integrations, or client deliverables.
Own metric governance: a single set of business definitions and KPIs with clear owners, implemented once in the semantic layer and consumed consistently by BI, client reporting, and AI.
Design how clients access their reporting and analytics data, including governed data sharing and extracts, partnering with product engineering where access runs through application APIs, with appropriate tenant isolation, security, and performance.
Set standards for data quality, governance, privacy, security, and data residency, including PII classification, retention, lineage, and access control, in support of our security and compliance programs.
Establish a data quality and validation framework, including automated reconciliation, testing, and certification of trusted datasets and AI-generated outputs, so that our teams and clients can trust results ine the reporting operating model for standard and client-specific reporting, including intake and prioritization, move clients toward governed self-service analytics, and deliver executive-level insights and advanced analytics, such as forecasting, where they create clear business value.
Build data migration and onboarding into a repeatable capability, with a reusable conversion framework, reconciliation, and post-migration validation, led by a dedicated migration lead.
Build AI-ready data foundations so that AI features call governed data and semantic models rather than calculating on their own, lead AI…
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