Data Analyst Role

OpenDataJobs•Washington, DC

About The Position

The work Data Analysts make operational data answerable. They work with stakeholders to define measures, acquire and reconcile data, investigate discrepancies, and turn recurring questions into analyses, reports, semantic models, and dashboards that people can use. The daily craft combines SQL, spreadsheets, scripting, and business intelligence tools with careful domain reasoning. Data Analysts profile and clean data, define calculations, identify trends and exceptions, validate results against source systems, and present the finding in language suited to the decision. What you'll build: * Recurring reports and dashboards with documented measures, refresh schedules, filters, alerts, and access controls. * Clean analytical datasets and semantic models that reconcile source fields, business definitions, relationships, and calculation logic. * Queries, scripts, and repeatable workflows that replace manual reporting and make transformations easier to test and review. * Variance, trend, cohort, performance, financial, program, or operational analyses that identify what changed and where to investigate. * Data-quality checks, reconciliation reports, issue logs, and documentation that establish where a number came from and whether it is fit for use. Who you are: You are precise with numbers and curious about what sits behind them. An unexplained variance is an invitation to trace definitions, filters, timing, and source records until the result makes sense. You can shift between a stakeholder's question and the data needed to answer it. You communicate clearly, design for the people who will use the analysis, and distinguish a useful operational conclusion from a claim the data cannot support.

Requirements

Practical skill in querying, cleaning, joining, aggregating, and validating data with SQL, spreadsheets, scripting, or comparable analytical tools
Experience defining measures, documenting business rules, reconciling sources, and building reports or dashboards that refresh reliably
Working knowledge of descriptive statistics, trend analysis, outlier investigation, data modeling, and visualization selection
Habits that make analysis reproducible, including version control where appropriate, documented transformations, quality checks, and peer review
The ability to present findings, assumptions, limitations, and recommended follow-up to technical and nontechnical audiences

Responsibilities

Define measures
Acquire and reconcile data
Investigate discrepancies
Turn recurring questions into analyses, reports, semantic models, and dashboards
Profile and clean data
Define calculations
Identify trends and exceptions
Validate results against source systems
Present findings in language suited to the decision
Build recurring reports and dashboards with documented measures, refresh schedules, filters, alerts, and access controls
Build clean analytical datasets and semantic models that reconcile source fields, business definitions, relationships, and calculation logic
Build queries, scripts, and repeatable workflows that replace manual reporting and make transformations easier to test and review
Perform variance, trend, cohort, performance, financial, program, or operational analyses that identify what changed and where to investigate
Create data-quality checks, reconciliation reports, issue logs, and documentation that establish where a number came from and whether it is fit for use