Sr. Software Architect

Position Summary

Neumo's revenue compliance portfolio spans several products that each solve part of the same problem on a different foundation: the Neumo Tax platform, FileLocal, short-term rental compliance, compliance auditing, payments, and a set of acquired legacy tax products. Together they serve more than 4,500 government agencies and process over $200 million in transactions each month. Each holds its own version of the same entities — a taxpayer, a jurisdiction, an obligation, a filing, a payment — under different names, in different shapes, with no shared definition between them.

The Principal Data Architect owns that problem. The role covers three connected things: the canonical data models and definitions that let the portfolio be understood as one estate rather than six; the warehouse, pipeline and reporting layer that serves analytics and operational insight across it; and the data interchange that keeps information flowing correctly between the existing platforms and whatever they converge onto.

It is a hands-on architecture role. The person in this seat writes the models, proves them against real data, and stays with them through implementation — but their primary leverage is getting seven engineering teams, and eventually other product domains, to build against a shared understanding of what the data means.

Why This Role Exists Now

No shared definition of data across the portfolio

There is no master data management practice and no common entity layer. Each product defines its own taxpayer, its own jurisdiction, its own filing. Every integration, every migration and every cross-product report pays the cost of reconciling those definitions by hand, every time. Unification and standardization are stated company priorities, and they are not achievable without this layer existing first.

No dedicated data capacity, and a reporting load carried by application engineers

Manual data clean-up, diagnostic queries and ad-hoc report requests consume engineering hours that belong on the roadmap. Analytics runs on Redshift at the platform level and on part-time and contract capacity inside the domain. There is no architect accountable for the pipeline, the models or the serving layer, and no plan that connects them.

Converging platforms, with data as the binding constraint

Twelve single-tenant to multi-tenant migrations are queued. FileLocal, short-term rental and legacy tax products are each slated to converge onto a common platform over the next two years, and payments and legacy tax systems interconnect with all of them. Each convergence is a data mapping and reconciliation problem before it is anything else. Today no one owns those mappings, and no one can prove a converted tenant is whole.

Fifteen years of history with no lifecycle policy

Tax data is retained indefinitely in the operational hot path — including years of history that almost no user touches — under service commitments that assume unbounded growth. Nothing can be deleted; state records-retention obligations require that archived records stay complete and retrievable. The architecture for how data ages, where it lives and how it is reached does not exist yet.

No record of why the architecture is what it is

Architecture decisions are not recorded. Priorities and plans shift, people move on, and the reasoning behind a given design is lost with them. Establishing that discipline for data is part of this role from day one.

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Key Responsibilities

Canonical models and master data

• Define the common entity model across the revenue compliance portfolio — taxpayer, business, jurisdiction, obligation, filing, payment, licence — with agreed definitions, ownership and systems of record.
• Establish data domains and a master data management practice proportionate to the organization: what is mastered where, how identity is resolved across products, and how conflicts are settled.
• Drive adoption of those definitions across engineering teams and product lines, recognizing that the model only matters once systems are actually built against it.

Warehouse, pipeline and reporting

• Own the architecture of the analytical estate: ingestion and pipeline design, modeling layers, the serving layer, and the boundary between operational and analytical workloads.
• Move recurring reporting and diagnostic query load off the operational databases and off application engineers, and establish self-service where it is safe to do so.
• Set standards for pipeline reliability, data freshness, lineage and cost, with observability sufficient to know when something has broken before a customer reports it.
• Establish data quality contracts, validation and monitoring, and drive known quality issues to root cause rather than to repeated clean-up.

Interchange between platforms

• Design the data flow between the existing tax platform, the legacy tax products, payments, short-term rental and any future unified platform — the contracts, the direction of travel, and which system owns the record at each stage.
• Own the conversion architecture for product and tenant migrations: the mapping from each source model into the target, and the reconciliation evidence that proves a migrated tenant is complete and correct.
• Design for the coexistence period, when old and new platforms run at the same time and data must stay consistent between them for an extended stretch.
• Set schema, versioning and mapping standards for external integrations — state revenue departments, municipal ERP and financial systems, banking and ACH, GIS, assessor systems and marketplaces — so each is built once rather than per customer.

Data lifecycle, archival and retention

• Author the data retention and archival architecture: what stays in the operational path, where older records live across storage tiers, how they are retrieved, and what performance applies to each tier.
• Partner with Legal, Product and Customer Success to turn state records-retention obligations and existing service commitments into a policy that can be communicated to customers — recognizing that each jurisdiction defines 'current' differently.
• Specify the application behaviour archival depends on: detecting out-of-window requests, retrieving asynchronously, and notifying the user when results are ready.

Governance and decision records

• Establish architecture decision records for data, so the reasoning behind a model or a migration survives the people who made it.
• Stand up data governance that enables delivery rather than gating it — reference implementations, patterns and training that teams can use without waiting on a committee, with oversight sitting on top rather than in the path.
• Define the reference and test data strategy, including realistic synthetic datasets for performance testing, load testing and demonstration environments.

Technical leadership

• Act as the senior technical authority on data across seven engineering teams and roughly fifty engineers, without formal authority over them.
• Produce reference architectures and sequenced roadmaps that leadership can fund and engineers can execute.
• Mentor engineers, analysts and database administrators, and raise the organization's data literacy.
• Represent data architecture in customer escalations, security reviews, procurement responses and executive planning.

Required Qualifications

• Twelve or more years in data engineering and architecture, including five or more years in an architect or principal-level role owning data architecture for a production estate at scale.
• Demonstrated ownership of a multi-source consolidation: several systems with incompatible data models brought onto a common canonical model, carried through to adoption rather than designed and handed off.
• Deep experience with master data management and enterprise data modeling — data domains, entity definition, identity resolution and the organizational work of getting teams to agree.
• Strong command of modern warehouse and pipeline architecture: ingestion, modeling layers, orchestration, serving, lineage and cost management at meaningful scale.
• Practical experience designing data lifecycle and archival strategies where records must remain retrievable rather than deleted, under regulatory retention requirements.
• Experience designing data interchange between systems that must stay consistent during an extended migration or coexistence period.
• Cloud-native data architecture experience on at least one major platform, with the willingness and ability to work in AWS — including S3 storage tiering, Redshift and the relational estate.
• Evidence of influencing engineering direction across teams without direct authority, and of communicating data decisions credibly to executives and to customers.
• Bachelor's degree in Computer Science, Information Systems or a related field, or equivalent professional experience.

Preferred Qualifications

• Experience in government technology, tax, financial services or another regulated domain with statutory retention and auditability requirements.
• Multi-tenant SaaS experience, including tenant isolation, configuration drift and single-tenant to multi-tenant consolidation.
• Experience establishing architecture decision records and a governance practice from scratch in an organization that had neither.
• Experience designing data foundations for AI or agentic workloads: lineage, retrieval, scoped access, evaluation datasets and audit trails.
• Familiarity with entity-attribute-value models and the trade-offs of reading from and migrating off them.
• Experience consolidating platforms acquired through M&A.