Analytics Engineer

What You Will Do

• Design, build, and maintain reliable analytics datasets, semantic models, and data products for engineering, manufacturing, testing, quality, and business use cases.
• Develop and optimize SQL- and Python-based data transformations that convert raw data into consistent, documented, and reusable analytical models.
• Proven ability to improve data quality, model performance, reporting adoption, and decision-making through cross-functional collaboration.
• Partner with business stakeholders, engineers, analysts, and data engineers to translate requirements into scalable analytics solutions and clear success criteria; document business logic, data lineage, model assumptions, and solution design; and share knowledge with global teams.
• Build and enhance dashboards, reports, and self-service analytics that communicate trends, performance, risks, and opportunities effectively.
• Create and maintain dimensional data models, metrics definitions, and data dictionaries to establish trusted and consistent reporting.
• Implement data quality checks, validation rules, monitoring, and reconciliation processes to improve the accuracy, completeness, and reliability of analytics data.
• Investigate data anomalies, perform root-cause analysis, and work with source-system owners to resolve data issues.
• Improve the performance, maintainability, and usability of analytical queries, pipelines, dashboards, and datasets.
• Use version control, code reviews, testing, and release practices to deliver governed and supportable analytics solutions.
• Protect sensitive data and follow applicable data security, access control, and governance practices.
• Continuously evaluate relevant technologies and analytical methods and contribute ideas for new capabilities and process improvements.
• Who You Are (Basic Qualifications)
• Bachelor's or master's degree in computer science, Data Science, Engineering, Information Systems, Statistics, or related technical field.
• 2-4 years of total experience, including at least 2 years of analytics engineering, business intelligence, data engineering, or a closely related role.
• Working knowledge of ETL/ELT patterns and data pipeline fundamentals.
• Strong SQL skills, including complex queries, joins, aggregations, window functions, query optimization, and the development of reusable data models.
• Practical experience using Python for data preparation, analysis, automation, and validation, with libraries such as Pandas and NumPy.
• Experience with source control tools such as Git and work-management or bug-tracking tools such as JIRA.
• Ability to define and validate business metrics, analyze trends, test assumptions, and communicate findings clearly.
• Strong analytical, problem-solving, documentation, and stakeholder collaboration skills, including the ability to explain complex technical concepts to global teams.
• What Will Put You Ahead
• Experience applying analytics in engineering, manufacturing, testing, or product-quality environments.
• Experience designing dimensional models and developing curated datasets, semantic layers, or data marts for reliable reporting and analytics.
• Experience with modern analytics engineering practices and tools, including dbt, automated testing, data contracts, orchestration, and CI/CD.
• Knowledge of statistical analysis, predictive analytics, anomaly detection, or machine-learning methods for generating actionable insights.
• At Koch companies, we are entrepreneurs. This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location. If you have questions, please speak to your recruiter about the flexibility and detail of our compensation philosophy.