BI Lab Manager

Job Description

Ecolab seeks a business-centric analytics professional who excels at turning ambiguous questions into actionable insights and has deep expertise in evolving those insights into scalable, production-ready solutions.

This role is the leader of a small team that sits at the intersection of business strategy, data, and product thinking. The primary strength of this role is the ability to quickly understand business context, translate it into meaningful metrics and analyses, and deliver relevant outputs that move decision-making forward.

In addition to leading the team, this individual will work iteratively with a variety of stakeholders across the enterprise. The day-to-day may include rapidly prototyping analyses, tools, or visualizations, refining them with stakeholders, and ultimately identifying which solutions should be scaled. As solutions mature, they will partner with BI developers and data engineers to operationalize them.

The person in this role will either facilitate the start of scaled solutions or they may contribute to hands-on delivery of scaled solution.

Success in this role is defined not by technical complexity, but by the ability to deliver relevant, impactful, data solutions.

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Key Responsibilities:

• Translate business needs into data-driven solutions
• Engage stakeholders to understand goals, context, and constraints
• Identify the most relevant questions, metrics, and drivers of value

• Rapidly prototype insights and analytical tools
• Move quickly from problem definition to gathering data to delivering an insight.
• Deliver early versions of analyses, dashboards, models, or lightweight tools either via PowerPoint, PowerBI, Streamlit, or custom AI tools.
• Iterate based on feedback to refine usefulness and clarity

• Apply the appropriate analytical approach
• Determine when to use visualization, statistical analysis, modeling, or simple heuristics to a business problem.
• Balance speed, rigor, and usability based on the situation (including the maturity of the question and the needs of the business).

• Perform data transformation and analysis
• Use SQL or Python to source, clean, and transform data into useable data for analysis. If engaged in scaling data transformation, leverage dbt to create a scaled and tested data model for consumption.

• Design meaningful metrics and outputs
• Define metrics that align with real business outcomes. Work with stakeholders to understand gaps in metric definitions or propose functional alternatives.