Data & Analytics Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data & Analytics Engineer based in the United States, working remotely within the Eastern Time Zone. In this role, you will help develop and maintain a modern cloud-based data platform that brings together business information from finance, HR, sales, customer relationship management, and service delivery systems. You will play a key role in consolidating legacy reporting environments into a centralized, governed source of truth. Working across the full data lifecycle, you will build reliable pipelines, design scalable data models, and deliver dashboards that support informed business decisions. You will also contribute to internal applications and AI-powered analytics that make business insights more accessible. Collaborating closely with technical leadership and cross-functional stakeholders, you will help improve data quality, operational efficiency, and reporting consistency. This is an opportunity to combine hands-on engineering, analytics, and innovation within a collaborative environment that actively embraces AI-assisted development. 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.