Analytics Engineer

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Lead, coach, and provide technical guidance to a team of analytics engineers
• Design and oversee scalable data pipelines, models, and architecture
• Partner with business and analytics stakeholders to turn complex questions into usable data solutions
• Build and automate data extraction, transformation, and loading (ETL) processes using industry standards
• Improve existing data pipelines by identifying performance, reliability, and quality issues
• Develop tools and standards to improve code quality, consistency, and development speed
• Create clear documentation to support long-term sustainability
• Collaborate closely with data visualization and reporting teams

Manager, Analytics Engineering

• Design and develop data Integrations to deliver Enterprise BI Data Marts and BI Semantic Data layer(s).
• Manage multiple projects within the team and support others within the DAS organization.
• Improve data processes, technology infrastructure and reporting/analytical services.

Senior Analytics Engineer

• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s)
• Develop modern data engineering pipelines leveraging AI assisted capabilities
• Lead multiple projects and provide technical guidance to team members
• Support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

Lead Analytics Engineer

• Partner with product, business, and engineering teams to identify data needs, define metrics, and enable data-driven decision making
• Design, build, and own scalable data pipelines and data models that power analytics, reporting, and product insights
• Develop and maintain core datasets for financial and usage behavioral analytics, enabling deep understanding of customer behavior, product adoption, and business performance
• Help define, instrument, and validate product and business metrics, including those exposed in customer-facing dashboards
• Drive improvements in data quality, reliability, and observability, including ownership of SLAs for critical pipelines
• Collaborate with engineering teams to evolve data models alongside product changes
• Contribute to the definition of our analytics layer (e.g., dbt models, semantic layer, metric definitions)
• Create and maintain clear technical documentation and data contracts

• Lead, coach, and provide technical guidance to a team of analytics engineers
• Design and oversee scalable data pipelines, models, and architecture
• Partner with business and analytics stakeholders to turn complex questions into usable data solutions
• Build and automate data extraction, transformation, and loading (ETL) processes using industry standards
• Improve existing data pipelines by identifying performance, reliability, and quality issues
• Develop tools and standards to improve code quality, consistency, and development speed
• Create clear documentation to support long‑term sustainability
• Collaborate closely with data visualization and reporting teams

• Work with Senior Business Intelligence and Analytics team members to design datasets and deliver insights that support our growing business.
• Partner with business leaders to develop scalable solutions and dashboards using Microsoft’s BI Stack and Python.
• Translate business needs into scalable data models and reporting layers that support consistent, reliable analysis.
• Document analytics requirements, data models and processes to ensure consistent and accurate reporting.

BI Analytics Engineer

• Manage the learning analytics platform and dashboard lifecycle, including backlog development, dependency coordination, and quality assurance
• Lead visualization and User Experience/User Interface (UX/UI) design discussions, providing guidance and recommendations to improve clarity, usability, and performance
• Gather, analyze, and document requirements, and translate them into user stories, use cases, and analytics specifications
• Plan and facilitate stakeholder interviews, discovery workshops, and design sessions to understand business processes and analytics needs
• Design, develop, and maintain complex analytic Power BI dashboards and reports to measure engagement, utilization, performance, ROI, and impact
• Translate learner usage data into insights that improve learning journeys, content effectiveness, and user experience
• Develop and optimize SQL queries, data models, and integrations across multiple platforms and data assets
• Identify data anomalies, recommend corrective actions, and assess the downstream impact of changes across platforms or teams
• Develop and execute testing and quality assurance plans to validate data accuracy and analytic outcomes
• Evaluate and apply ChatGPT Artificial Intelligence (AI) technologies to enhance learning dashboards, analytics automation, and reporting capabilities.

Senior Learning Analytics Engineer

• Own the data transformation layer between the raw database and all client-facing reports
• Audit, document, and map all existing stored procedures and SSIS jobs to their downstream reports and clients
• Build and maintain dbt models that wrap or replace legacy stored procedures over time
• Serve as the internal subject matter expert on all data transformation logic
• Partner with the BI/Report Developer to ensure clean, tested data reaches Power BI
• Define and enforce standards for new development — no new stored procedures created without review and approval
• Identify and resolve root-cause QA issues at the data layer before reports are delivered
• Present data pipeline health and migration progress to the MIS Director weekly
• Maintain a documented data dictionary for all key tables, fields, and transformation rules

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Analytics Engineer

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Analytics Engineer