Data Analyst

Powell•Houston, TX

13h•Onsite

About The Position

Powell Industries is undergoing a significant transformation in its data utilization strategy. The company is moving from traditional, often manual data management methods (like Excel exports and secondary processes) to a modern platform. This shift aims to de-silo data, enable better collaboration, and democratize data access. The Data Analyst will be central to this transition, translating business questions into reliable data products, promoting self-serve analytics, and reducing the time to insight. A key aspect of the role is making insights actionable within the data experience and integrating data entry and collection into a unified surface.

Requirements

Solid SQL skills for querying, filtering, joining, and aggregating in a cloud data warehouse environment.
Familiarity with modern data platform structures.
Working Python skills for scripting, automating data tasks, and notebook environments (Jupyter, Marimo, or similar).
An instinct for clarity in defining and presenting metrics.
Comfort with ambiguity and turning vague business asks into concrete questions.
Curiosity about AI tooling and its application in making data accessible.

Nice To Haves

Experience with Sigma Computing or comparable modern BI platforms (Tableau, Looker, Power BI).
Exposure to dbt or semantic modeling concepts.
Experience in a manufacturing, industrial, or operations-heavy environment.
Familiarity with pipeline orchestration or workflow automation tools.

Responsibilities

Own and evolve semantic data models and governed views to serve as a trusted foundation for business-wide workbooks, apps, and workflows.
Enable business users to work independently in Sigma by ensuring well-defined, documented, and expressive underlying data models.
Utilize AI tools for prompt-driven query generation, notebook-based analysis, and faster pattern recognition to accelerate insights.
Curate and maintain common definitions, reusable metrics, and shared business logic for organizational consistency.
Integrate unstructured data (documents, emails, field notes) into the analytical surface alongside AI models to answer complex business questions.
Help the organization differentiate between operational metrics and strategic analytics, influencing what gets built and how it's updated.
Work iteratively with business teams to understand their underlying needs and translate them into improved data models, definitions, and questions.
Identify and automate manual, recurring data workflows (e.g., weekly reports, monthly summaries) into auditable pipelines.
Build reusable, parameterized notebooks and templates to make expert-level analysis accessible.
Write clean SQL and Python for automation, data validation, and transformation on the cloud data platform.