• Report to our Engineering Manager • Define and own the long-term data architecture strategy for Unqork's core platform, covering data modeling, query design, storage topology, and access patterns across a complex data environment that includes MongoDB as well as integration with Relational and Columnar database models • Ensure the data layer meets the security, performance, scalability, and resilience requirements of enterprise-grade, mission-critical applications. • Evaluate and recommend the right database technologies, indexing strategies, caching layers, ETL and search infrastructure for each class of workload — including when to use MongoDB Atlas Search, Caching (e.g., Redis), Querying (e.g., Kafka) and Streaming components • Own the end-to-end solutions around data transfers using solutions like ETL for customers. • Own the data architecture for Unqork's AI-driven development layer — defining persistence, versioning, and query standards for AI-generated configurations. • Lead the design of declarative data models and schemas that enable non-technical users to build complex logic while maintaining strict data integrity. • Define the architectural boundary between database-layer computation (aggregation pipelines, indexing) and application-layer computation (Node.js post-processing, in-memory caching), and establish standards for which work belongs where. • Create and maintain comprehensive documentation including data architecture blueprints, indexing governance policies, query standards, and migration playbooks. • Own capacity planning and cost modeling for data infrastructure resources as Unqork scales. • Mentor and grow a team of data engineers and database engineers responsible for the health and performance of Unqork's data platform. • Establish data modeling best practices and enforce standardization across all environments — including schema conventions, index lifecycle management, and pagination contract design. • Oversee the design and operation of Unqork's database infrastructure — defining thresholds, coverage policies, write amplification limits, and manual override processes. • Drive data operational excellence by implementing and refining query performance monitoring, slow query alerting, explain plan review processes, and incident response playbooks for database degradation events. • Define and enforce data access governance — including RBAC data model standards, cache TTL policies, and the rules under which eventual consistency is acceptable vs. when strong consistency is required. • Comply with security regulations while working on data designs and patterns for Unqork platform. • Partner with Product to translate product requirements into data model decisions, and identify where relaxing a product constraint unlocks a disproportionate architectural improvement.

• Lead a team that owns the AI self-service portal and the related set of microservices. • Work with a cross-functional team of engineers to contribute to our data platform. • Build and support a distributed platform supporting all ExParte data. • Work across our product, primarily on the data pipelines. • Interface directly with internal teams. • Evaluate software and implementation options and document them for technical teams. • Work with data analysts to collect insight on possible data collection efficiencies and identify automation potential for manual workflows. • Integrate best qualitative practices in program design and development. • Be a part of a distributed team (we’re in North America and Europe). • Work with Azure cloud and Databricks. • Develop technical architectures and specific implementations to meet business needs. • Guide the team’s software engineering best practices by documenting standards and completing code reviews. • Troubleshoot new and existing code and provide feedback and solutions to structural issues in the codebase as they arise. • Advise on the feasibility of nonfunctional requirements and ensure the successful implementation of features while meeting those requirements.

• Work directly with product owners and data experts to build products that solve complex client problems • Build and support a distributed platform supporting all ExParte data • Work across our product, primarily on the data pipelines • Interface directly with internal teams • Evaluate software and implementation options and document them for technical teams • Work with data analysts to collect insight on possible data collection efficiencies and identify automation potential for manual workflows • Integrate best qualitative practices in program design and development. • Be a part of a distributed team (we’re in North America and Europe) • Work with Azure cloud and Databricks • Develop technical architectures and specific implementations to meet business needs.

• Work directly with product owners and data experts to build products that solve complex client problems • Build and support a distributed platform supporting all ExParte data • Work across our product, primarily on the data pipelines • Interface directly with internal teams • Evaluate software and implementation options and document them for technical teams • Work with data analysts to collect insight on possible data collection efficiencies and identify automation potential for manual workflows • Integrate best qualitative practices in program design and development • Be a part of a distributed team (we’re in North America and Europe) • Work with Azure cloud and Databricks • Develop technical architectures and specific implementations to meet business needs • Guide the team’s software engineering best practices by documenting standards and completing code reviews • Troubleshoot new and existing code and provide feedback and solutions to structural issues in the codebase as they arise • Advise on the feasibility of nonfunctional requirements and ensure the successful implementation of features while meeting those requirements