Data & Analytics Engineer

Accountabilities: Develop and maintain the data platform: Engineer, operate, and extend a cloud-based data platform built on Microsoft Azure, integrating information from business applications and SaaS systems into a centralized lakehouse architecture. Build and optimize data pipelines: Develop and maintain reliable ELT pipelines using SQL, Python, REST APIs, and appropriate data processing technologies to ensure accurate, timely, and consistent data availability. Lead reporting migration initiatives: Drive the transition from legacy SQL Server reporting infrastructure and Power BI toward a centralized lakehouse and data portal, migrating active reports and ensuring continuity throughout the process. Retire legacy reporting systems: Identify dependencies, coordinate with stakeholders, validate replacement solutions, and manage the controlled decommissioning and archival of legacy systems. Design robust data models: Build and maintain dimensional models, medallion architectures, slowly changing dimensions, and standardized business metrics to support consistent reporting and analytics. Ensure data quality and reliability: Monitor pipeline health, investigate discrepancies, reconcile metrics across systems, and implement validation and observability practices that maintain confidence in business reporting. Deliver actionable reports and dashboards: Develop and enhance dashboards and reporting solutions that help teams monitor service delivery, profitability, customer retention, sales opportunities, and operational performance. Translate business needs into analytics solutions: Work with stakeholders across Service Delivery, Finance, Sales, Client Success, and Leadership to clarify reporting requirements and transform business questions into measurable, well-defined metrics. Develop internal applications: Contribute to internal tools and web applications that leverage the data platform, improving access to information and supporting operational workflows. Build AI-powered analytics capabilities: Help introduce AI features that enable users to explore business data through natural-language questions, leveraging large language models, semantic layers, and retrieval-based approaches where appropriate. Maintain engineering standards: Use Git, pull requests, code reviews, and automated deployment practices to deliver maintainable, reliable, and well-documented solutions. Leverage AI-assisted development: Use AI coding assistants to accelerate development, investigate data, review code, and produce documentation while carefully validating outputs and taking ownership of the quality and correctness of delivered work. Support business systems as needed: Assist with data-related requirements across connected business applications, integrations, and operational systems to maintain a cohesive analytics environment. Requirements Relevant professional experience: At least 3 years of experience in data engineering, analytics engineering, or a closely related technical role. Advanced SQL skills: Strong SQL proficiency, including T-SQL, with the ability to understand, troubleshoot, refactor, and optimize stored procedures, database views, and SQL Server Agent jobs. Python expertise: Strong Python programming skills for data processing, transformation, and analytics, with experience using tools such as DuckDB, Spark, pandas, or comparable technologies. Cloud data platform experience: Hands-on experience with cloud-based data platforms, ideally Microsoft Azure, and familiarity with Delta Lake or similar data storage and table formats. Data modeling knowledge: Solid understanding of dimensional modeling, medallion architecture, slowly changing dimensions, and the development of consistent, reusable business metrics. Data integration expertise: Experience building and maintaining ELT pipelines that integrate REST APIs, SaaS applications, and multiple business data sources. Migration experience: Demonstrated success migrating reports, databases, or data workloads between platforms, including systematic reconciliation, validation, stakeholder communication, and formal sign-off. Business intelligence and reporting: Experience developing dashboards and reports for business users, translating ambiguous questions into precise metrics, and ensuring outputs are accurate and actionable. Engineering best practices: Familiarity with Git-based workflows, pull requests, code reviews, and collaborative software development practices. Analytical mindset: Strong problem-solving skills and a disciplined approach to validating data, investigating discrepancies, and reconciling results before accepting them as accurate. Communication and collaboration: Ability to work effectively with technical teams and nontechnical stakeholders, understand business priorities, and communicate complex data concepts clearly. Preferred industry experience: Experience in a managed service provider (MSP) or IT services environment, or familiarity with ConnectWise, Sage Intacct, or HiBob data. Preferred business systems knowledge: Experience working with Salesforce, Gong, Cognism, or HubSpot, either as an administrator or through data integration and analytics. Preferred Power BI expertise: In-depth knowledge of DAX, semantic models, REST and XMLA APIs, and usage metrics to support the migration and eventual replacement of existing Power BI reporting. Preferred legacy system decommissioning: Experience identifying system dependencies, coordinating stakeholders, managing transitions, and archiving legacy technology. Preferred modern data and cloud tools: Familiarity with Microsoft Fabric, Azure Container Apps, Docker, Azure Static Web Apps, or Azure App Service. Preferred web development skills: Experience with JavaScript, HTML, and CSS to support internal application development and data portal enhancements. Preferred data quality tooling: Experience with dbt tests, Great Expectations, custom validation frameworks, or other data quality and observability solutions. Preferred AI and automation experience: Familiarity with LLM APIs, retrieval-augmented generation (RAG), semantic layers, and the development of AI-powered data applications. Preferred deployment automation: Experience implementing CI/CD workflows with GitHub Actions or similar tools. AI-first development approach: Willingness to use AI-assisted development tools regularly, critically evaluate their outputs, and maintain high standards of accuracy, security, and code quality. Benefits Competitive compensation: Competitive annual salary. Performance incentives: Quarterly bonus opportunities. Paid time off: Progressive paid time off (PTO) policy. Healthcare coverage: Medical, dental, and vision insurance options. Additional insurance protection: Life and disability coverage options. Retirement savings: 401(k) plan with company matching. Career development: Access to career development resources, coaching, and opportunities to expand technical expertise. Remote work: Remote position within the United States, with an Eastern Time Zone working arrangement. Modern technology environment: Opportunity to work with cloud data platforms, contemporary engineering tools, and AI-assisted development practices. Meaningful technical ownership: Hands-on involvement in a major data platform evolution, reporting modernization, and the introduction of AI-powered analytics capabilities.