Data Analyst Lead

Oshkosh•Oshkosh, WI

About The Position

The Data Analyst Lead works across business intelligence, data engineering, and domain analytics to advance Oshkosh’s analytics maturity. The role turns complex enterprise, supply chain, and manufacturing data into decision-ready analytical assets that support operational efficiency, cost reduction, and business growth. It partners with business stakeholders across the enterprise to convert strategic questions into scalable data products. The role architects and maintains data transformation pipelines and dimensional models, applies software engineering practices to analytics, standardizes business definitions, and curates the enterprise semantic layer. It also optimizes query performance and warehouse resource use, leads modern analytics engineering practices, and serves as a technical liaison between Data Engineering teams and business stakeholders. The Data Analyst Lead partners with business unit leaders, data scientists, and BI developers to deliver data models, dashboards, self-service datasets, and reporting frameworks. The role also enforces data governance, role-based access controls, and compliance policies for reporting datasets.

Requirements

Bachelor’s or master’s degree in computer science, Information Systems, Business Analytics, Mathematics, Engineering, or a related discipline.
At least 7 years of experience in data analytics, business intelligence, or data engineering.
At least 2 years of demonstrated leadership experience, including leading technical projects, mentoring analysts, or serving as a team lead.
Advanced mastery of complex SQL, including window functions, performance tuning, query profiling, and dynamic modeling.
Hands-on experience with modern cloud data platforms such as Databricks, Azure Synapse, or BigQuery.
Proficiency with Git/GitHub, CI/CD pipelines, and command-line interfaces.
Advanced data visualization and data modeling experience with Power BI or similar tools.
Expertise with dbt and Kimball dimensional modeling techniques, including Star and Snowflake schemas.
Working proficiency in Python for lightweight data scripting, API ingestion, or automation.
Ability to translate ambiguous business requirements into robust, production-grade analytical data models.
Exceptional communication and storytelling skills, including explaining technical architectures to business executives and business context to engineers.
Strong product mindset and empathy for end-user analytics workflows.

Nice To Haves

Experience within manufacturing, industrial IoT, or supply chain environments.

Responsibilities

Architect, build, and maintain scalable, performant data transformation pipelines and dimensional models using cloud data warehouses or lakehouses and dbt.
Apply analytics software engineering best practices, including Git version control, modular SQL, code reviews, regression testing, and CI/CD deployment.
Standardize business definitions and curate the enterprise semantic layer to provide consistent metric reporting across business units.
Optimize analytical query performance, warehouse resource consumption, and end-user access patterns.
Lead modern analytics engineering practices, advanced SQL techniques, and data modeling frameworks.
Serve as the technical liaison between upstream Data Engineering teams and downstream business stakeholders.
Establish data documentation standards and lineage tracking across projects.
Partner with business unit leaders, including Supply Chain, Manufacturing Operations, and Lifecycle Services, to understand reporting requirements and translate them into data models.
Partner with data scientists and BI developers to design dashboards, self-service datasets, and reporting frameworks, including in Power BI.
Enforce data governance, role-based access controls, and compliance policies across reporting datasets.