Data Analytics Engineer Jobs in Ohio (NOW HIRING)

The Data Analytics and BI Technical Lead strengthens NAQ market-quality issue detection and promotion capability by managing data engineering methods, BI solutions, analytics tools, and governance processes enable the unit to identify field-quality trends quickly, accurately, and consistently. This role serves as a department and unit-level technical expert by translating complex data into reliable business insight, improving analytical capability across responsible areas, supporting business planning decisions, and helping standardize quality analytics methods that improve speed, consistency, forecasting, and market-quality response.

• Business planning and capability development - Manage Unit and Department business planning initiatives that improve the processes, tools, reporting standards, and analytical methods used to detect market quality trends and promote issues quickly. Responsible for identifying gaps in existing business capability, defining the strategy for resolution, aligning stakeholders, and leading the team required to execute countermeasures that support Unit and Department objectives.
• Analytics tools, platforms, and technical standards - Manage and continuously improve the data engineering toolset, including BI platforms, Excel, MS Access, SQL, Python, and appropriate AI-enabled solutions. Responsible for establishing department and unit BI solutions, data architecture, data quality, reporting methodologies, and reusable analytics models that improve speed, consistency, accuracy, repeatability, and scalability across responsible teams.
• Recurrence prevention, innovation, and process improvement - Lead recurrence prevention activity for gaps identified in responsible business areas, translating lessons learned into durable process changes, countermeasures, and follow-up controls.
• Quality record governance, approval, and advisory responsibiliy - Review and approve QIS, QIC, and Problem Definition records to ensure issue logic, scope, data evidence, and documentation quality are aligned before promotion. Serve as a Department and Unit expert advisor on issue detection methodologies, emerging quality risks, analytics strategies, data quality standards, and appropriate use of market-quality data sources.
• High-complexity leadership data requests and strategic recommendations - Develop strategies to manage and execute complex or high-profile ad-hoc data requests from internal or external leadership, ensuring outputs are clear, accurate, timely, and decision-ready. Present strategic analytics recommendations to Unit and Department leadership.

Minimum Educational Qualifications

• BS in Computer Science, Data analytics, Mathematics, Economics, or Engineering with strong engineering aptitude or equivalent experience

Minimum Experience

• 8+ years analytical or information analysis experience, based on amount of education

Other Job-Specific Skills

• Expert in utilizing NAQ data sources and practical ability to extract, validate, summarize, and explain complex market-quality data.
• Responsible for training, mentoring, and advising data engineers and cross-functional users on proper data interpretation, repeatable analysis methods, and decision-ready communication.
• Advanced proficiency with BI tools, including QlikView, Qlik Sense, and Power BI.
• Experience with SQL, Python, and common data science libraries for analysis, automation, and repeatable reporting.
• Experience programming VBA and supporting MS Access and Excel-based analytical tools.
• Expert knowledge of CoReMQ and related records, including QIC, QIS, Problem Definitions, Warranty Claims, F&S Queries, and Tech Line.
• Strong problem-solving, analytical reasoning, and applied statistics skills, including distributions, statistical testing, regression, and trend evaluation.
• Ability to communicate findings clearly in written, verbal, and visual formats for technical and leadership audiences.
• Strong prioritization, attention to detail, and judgment when managing competing requests and business-critical outputs.
• Expert in gathering, cleaning, transforming, and reconciling data from multiple sources into reliable analysis-ready datasets.
• Advanced experience developing department and unit analytics strategy, predictive modeling approaches, BI governance standards, data architecture recommendations, and reusable analytics best practices for responsible teams.