Analytics Engineer

• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.
• Ensure semantic objects conform to approved business taxonomy, metric definitions, and naming conventions.
• Gather and clarify requirements from commercial business users to ensure dashboard and reporting solutions meet their analytical needs.
• Provide first-line support for BI platform questions, report issues, and data interpretation queries from business users.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to the appropriate pipeline owners.
• Support the development and testing of agentic AI workflows that leverage governed semantic layer assets and BI data products for automated commercial insights delivery.

Commercial Analytics Engineer – Business Decision Intelligence

• 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, such as Databricks GenAI features
• Lead multiple projects, provide technical guidance, and support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

Lead Analytics Engineer

• Design, develop, and maintain data pipelines and infrastructure using AWS Services, MWAA Airflow, Snowflake, GIT and Azure DevOps CD-CI pipelines.
• Work with other team members to ensure the performance, reliability, and scalability of our data systems
• Keep up to date with industry trends and best practices related to data engineering and related technologies
• Troubleshoot and debug issues with data pipelines and systems
• Write and maintain technical documentation for data engineering processes and systems

Analytics Data Engineer

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Provides financial and regulatory reporting and analyses to maintain adequate controls over the financial and regulatory reporting processes
• Responsible for running complex business performance, risk and operational analytics
• Develops analytical methods/models to assess market, credit and/or operational risk of new and existing financial products
• Leverages business/product expertise to rigorously analyze large datasets, improve risk adjusted returns, deliver profitable growth, and communicate conclusions
• Synthesizes analytical results and develops, recommends, and implements business strategies that improve lending decisions, assist in managing risk, increase revenues, reduce exposure to losses, meet business goals, and improve performance
• Establishes baselines for strategies and tracks actual performance to expectations
• Applies predictive models, third party data, and other tools to develop and execute appropriate segmentation and targeting for acquisition and portfolio strategies to provide insight into portfolio risk
• Manages engagements with internal and external information suppliers ensuring solution is fit for purpose while maintaining appropriate governance and oversight
• Works with business, credit, data, and model development partners to design, develop, and monitor test designs and analytical reporting to track and enhance strategies
• Designs/enhances standard reporting suites for regular product/portfolio reviews
• Collaborates with the line of business, Finance, and Risk partners to assess and establish credit risk appetite and to understand its implications, as well as to establish policies and procedures governing the identification, monitoring, and management of risk appetite.

Portfolio Analytics & Strategy Specialist – Data, Modeling & Analytics

• Lead, manage, and develop a high-performing team of Analytics Analysts
• Define and own the enterprise semantic layer strategy for commercial sales analytics
• Own the global self-service analytics operating model
• Lead the evaluation, selection, and adoption of semantic layer, BI, and analytics platform capabilities
• Define and drive the enterprise strategy for agentic AI integration within the commercial analytics platform
• Own and drive the strategic transformation of commercial analytics from reactive dashboard reporting to proactive, AI-ready data products
• Build and maintain trusted relationships with senior commercial leaders across Regions and Business Units

Associate Director, Global Sales Analytics Engineering – Business Decision Intelligence

• The Analytics Analyst designs, builds, and maintains dashboards, reports, and semantic layer assets that enable commercial business users to access governed, accurate insights.
• Working within defined data products and under guidance from senior analysts and leads, this role translates business requirements into self-service BI solutions and maintains the quality and usability of the analytics user interface.
• Dashboard & Report Development: Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Semantic Layer Development & Maintenance: Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.

Commercial Analytics Engineer – Go To Market Analytics

• Lead the design and implementation of governed semantic layer models aligned to enterprise business taxonomy, metric definitions, and data governance standards
• Define and enforce semantic layer design patterns, naming conventions, and object hierarchies that enable consistent metric consumption across all BI and analytics tools
• Partner with Commercial Data Product Strategy and Analytics Engineering teams to ensure physical data models are correctly surfaced through the semantic layer
• Maintain semantic layer health, resolving conflicts, deprecating legacy objects, and managing version-controlled releases
• Design and deliver advanced, scalable BI dashboards and user interfaces that serve as authoritative commercial reporting surfaces for senior business stakeholders
• Drive the reduction of bespoke report requests by designing modular, reusable analytics assets that empower business users to self-serve
• Act as a primary analytics liaison between commercial business teams, Data Product Strategy, and Analytics Engineering to align reporting needs with data model capabilities
• Lead the design and delivery of agentic AI workflows that consume semantic layer assets to automate commercial reporting, anomaly detection, and insight generation.

Senior Commercial Analytics Engineer – Business Decision Intelligence

• Define and drive the enterprise semantic layer strategy for commercial analytics
• Own the global self-service analytics strategy
• Define UX/UI standards and design principles for the commercial analytics interface
• Partner with Commercial Data Product Strategy to co-own the enterprise business glossary
• Define and drive the enterprise strategy for agentic AI integration within the commercial analytics platform
• Provide strategic technical leadership across the analytics team

Principal Analytics Engineer – Business Decision Intelligence

• The Analytics Analyst designs, builds, and maintains dashboards, reports, and semantic layer assets.
• Work within defined data products and under guidance to translate business requirements into self-service BI solutions.
• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform.
• Gather and clarify requirements from commercial business users to ensure analytic needs are met.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to pipeline owners.

• Design and build data products that generate actionable insights for both internal stakeholders and external customers
• Develop and deploy AI agents and MCP-powered tooling to enable self-service access to data across the organization
• Own and contribute to key OKRs through data-led initiatives, partnering closely with Product Leadership and cross-functional teams
• Improve data quality, documentation, and governance practices to ensure the data layer is accurate, trusted, and AI-ready
• Lead exploratory and ad-hoc analyses for product and leadership partners, synthesizing complex data into clear, decision-ready recommendations
• Build and maintain BI infrastructure in Looker and Amplitude, including dashboards, explores, and self-service reporting layers
• Act as a data thought partner to product teams, proactively surfacing insights, flagging risks, and shaping strategy through a data lens

Data Analytics Specialist

• 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, such as Databricks GenAI features and developer productivity tools (e.g., GitHub Copilot)
• Lead multiple projects, provide technical guidance, and support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

• 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.

Analytics Engineer jobs in Illinois

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Commercial Analytics Engineer – Business Decision Intelligence

Lead Analytics Engineer

Analytics Data Engineer

Analytics Engineer

Portfolio Analytics & Strategy Specialist – Data, Modeling & Analytics

Associate Director, Global Sales Analytics Engineering – Business Decision Intelligence

Commercial Analytics Engineer – Go To Market Analytics

Senior Commercial Analytics Engineer – Business Decision Intelligence

Principal Analytics Engineer – Business Decision Intelligence

Commercial Analytics Engineer – Business Decision Intelligence

Data Analytics Specialist

Lead Analytics Engineer

Analytics Engineer