Data Analyst

Vontier•Stow, OH

13d•$85,000 - $110,000•Hybrid

About The Position

The Data Analyst (Mid-Level) partners with business stakeholders to translate questions into clear, actionable analytics use cases and delivers a first pass of SQL code and datasets to support reporting and decision-making. The role applies data modeling best practices and leverages ETL/data transformation tools to create reusable, well-structured data assets. This position will also support agile execution by helping facilitate Scrum ceremonies and maintaining transparency and flow of work through Azure DevOps.

Requirements

3–6 years of experience in a Data Analyst / BI Analyst / Analytics role delivering business-facing analytics solutions.
Strong SQL skills (joins, CTEs, aggregations, window functions) with ability to debug and improve query logic/performance.
Proven experience gathering requirements and translating business questions into clear deliverables (datasets, analyses, or reporting-ready outputs).
Working knowledge of data modeling concepts (grain, keys, relationships; familiarity with dimensional modeling principles).
Experience working in an agile environment and facilitating or co-leading Scrum ceremonies; proficiency managing work in Azure DevOps (Boards/Backlogs).

Nice To Haves

Experience with ETL/data transformation tools and repeatable transformation workflows (tool-agnostic).
Experience with a modern data warehouse or analytics platform (e.g., Snowflake, BigQuery, Redshift, Databricks, SQL Server).
Experience with BI/reporting tools (e.g., Power BI, Tableau, Looker) and understanding downstream reporting impacts of dataset design.

Responsibilities

Partner with business stakeholders to understand objectives, define the problem, and translate requests into clear analytics use cases and requirements.
Define, document, and align on metrics/KPIs, including assumptions and acceptance criteria.
Help run Scrum ceremonies (standups, backlog refinement, sprint planning, demos, retrospectives) and drive follow-ups on action items.
Manage work items in Azure DevOps (intake, prioritization support, status updates), surface blockers/risks, and communicate progress to stakeholders.
Perform data discovery to identify relevant source systems, tables, and fields; assess data availability and quality.
Develop first-pass SQL queries and datasets to address business requests; iterate quickly based on feedback.
Validate outputs using QA techniques (reconciliations, spot checks, trend checks, edge-case testing) and document findings/limitations.
Apply data modeling fundamentals to produce analytics-ready datasets (e.g., correct grain, keys, relationships; fact/dimension concepts where appropriate).