We are looking for a skilled Data & Integrations Engineer to strengthen enterprise data infrastructure and streamline connections across business systems. In this role, you will take ownership of a Microsoft Azure data warehouse environment, overseeing ETL pipelines, data architecture, semantic models, and reporting infrastructure. You will work closely with Data Warehouse and Business Systems teams to design, develop, and optimize integrations between platforms such as NetSuite, HubSpot, and other operational systems. A key part of your mission will be driving the adoption of AI-assisted development tools to improve engineering productivity, code quality, and automation. You will also build and maintain internal applications, develop business-focused tools, and ensure reliable data flows across the organization. This is an exciting opportunity to combine hands-on engineering, cross-functional collaboration, and emerging AI technologies in a continuously evolving technical environment.
Manage, maintain, and enhance the Microsoft Azure data warehouse architecture, ensuring the reliability, scalability, security, and performance of data infrastructure.
Oversee ETL and ELT pipelines that collect and transform data from multiple internal sources, coordinating external consulting partners responsible for ongoing pipeline updates and maintenance.
Identify potential risks, bottlenecks, and improvement opportunities within data pipelines, strengthening system redundancy, consistency, and reliability.
Design and implement new database schemas, data pipelines, and semantic models to accommodate additional data sources and evolving reporting requirements.
Collaborate with data analysts and reporting teams to ensure data accuracy, query performance, and usability across reporting environments.
Troubleshoot and optimize SQL queries, stored procedures, pipeline jobs, and database processes.
Implement and maintain appropriate security, access management, and data protection controls across data warehouse environments.
Partner with Business Systems teams to design, develop, monitor, and improve integrations between NetSuite, HubSpot, other operational platforms, and the data warehouse.
Develop and maintain integrations using Celigo as the primary integration platform, while evaluating alternative technologies when appropriate.
Establish integration monitoring and alerting mechanisms, investigate synchronization failures, and resolve data discrepancies.
Evaluate and integrate new business systems and data sources using APIs, webhooks, and other integration technologies.
Support the configuration and extension of business applications whenever data or automation improvements are required.
Coordinate changes across Data Warehouse and Business Systems environments to prevent disruptions and maintain system compatibility.
Manage internally developed Azure-based applications, ensuring their ongoing reliability, security, and performance.
Collaborate with business stakeholders to identify requirements, develop new application features, and deliver solutions aligned with operational needs.
Diagnose and resolve application defects, conduct quality assurance testing, and validate new features and fixes before deployment.
Gather business requirements and develop internal tools, reports, workflows, and applications that improve operational efficiency.
Identify opportunities to automate repetitive manual processes across finance, sales, and operations.
Maintain, consolidate, or retire legacy tools and applications that are no longer aligned with current business requirements.
Integrate AI-assisted development tools into coding, code reviews, testing, and technical documentation while maintaining independent engineering judgment and accountability for results.
Explore emerging AI technologies, assess their practical value, and share effective techniques and best practices with technical teams.
Develop reusable prompts, utilities, connectors, and other AI-enabled resources to improve productivity and support consistent development practices.
Contribute to guidelines governing the safe and responsible use of AI tools with organizational data and systems.
Produce and maintain technical documentation, system definitions, operational runbooks, and implementation records.
Ensure all technical changes are thoroughly tested before release and follow established deployment and quality assurance procedures.