Cushman & Wakefield Global, Inc is looking for a Senior Data Analyst to support data-driven decision-making across the organization. You will be responsible for collecting, cleaning, analyzing, and interpreting large, complex datasets to transform raw data into actionable insights that improve operational efficiency, support strategic initiatives, and identify growth opportunities.

What You'll Do

Collect, clean, validate, and analyze large datasets from multiple internal and external sources.
Perform data profiling, quality checks, and validation to ensure accuracy and reliability.
Identify trends, patterns, and anomalies through exploratory and statistical analysis.
Translate analytical findings into actionable insights for business stakeholders.
Build and maintain dashboards, reports, and visualizations using tools such as Tableau or Power BI.
Write and optimize queries using SQL to extract and manipulate data from relational databases.
Work with data stored in data lakes, SQL Server databases, and Salesforce.
Support ad hoc analysis and recurring reporting needs across teams.
Collaborate with data engineering, product, research, and business teams to define requirements and ensure alignment.
Document data definitions, assumptions, and methodologies to support transparency and governance.

What We're Looking For

Bachelor’s degree in Data Analytics, Statistics, Economics, Computer Science, Finance, or a related field (or equivalent experience).
3+ years of experience in a data analyst or analytics-focused role.
Strong proficiency in SQL and Excel.
Experience with Tableau and/or Power BI for data visualization and dashboard development.
Working knowledge of Python for data analysis (e.g., pandas, numpy).
Experience performing data profiling, validation, and basic statistical analysis.
Ability to clearly communicate insights to non-technical stakeholders.

Nice to Have

Commercial real estate (CRE) experience or exposure to real estate, finance, or market data.
Experience working with Salesforce data models and CRM-based datasets.
Familiarity with data lakes and modern data architectures.
Experience supporting broker-facing, client-facing, or revenue-driving teams.
Familiarity with tools such as Ringlead (a plus, not required).
Experience working with imperfect or incomplete datasets and building trust in data over time.