Position:
Data Analyst
Company:
Cushman & Wakefield
Location:
Gurgaon, India
Job type:
Full-time
Job mode:
On-site
Job requisition id:
R276441
Years of experience:
0–3 years
Company description
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Cushman & Wakefield is a globally recognized name in real estate services, operating in more than 60 countries.
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With over 52,000 employees and nearly 400 offices worldwide, the company serves clients with high standards in real estate strategy, operations, and transactions.
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In 2023 alone, Cushman & Wakefield generated $9.5 billion in revenue, indicating both scale and trust in its offerings.
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The company works with building owners, occupiers, and investors to help them maximize property value through tailored solutions.
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Cushman & Wakefield is publicly traded on the New York Stock Exchange under the ticker symbol CWK.
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Their work spans multiple real estate domains, including asset services, capital markets, facility services, global occupier services, leasing, and valuation.
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The organization is driven by innovation, client satisfaction, and delivering measurable business impact through customized solutions.
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The company fosters an inclusive workplace, encouraging growth, diversity, and a performance-driven culture.
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They actively invest in cutting-edge technology to improve data analytics, sustainability, and operational efficiency.
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Their long-term strategy includes strengthening service lines and digital transformation for deeper market insights and better client support.
Profile overview:
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The Data Analyst will collaborate closely with program managers and department leaders to gather analytics requirements and deliver meaningful insights.
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The role requires managing key performance indicators (KPIs), identifying trends, and providing strategic data-driven suggestions.
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You will be expected to build intuitive dashboards and data visualizations that help stakeholders make informed decisions.
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The position will involve supporting revenue growth by enabling upsell and cross-sell strategies through targeted data analysis.
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Your analysis will include deep dives into operational inefficiencies and productivity gaps across business functions.
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You’ll be responsible for maintaining and improving data pipelines, sourcing from both primary and secondary sources.
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Strong understanding of system architecture and integration methods is important for scalable solution delivery.
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The role will demand skills in predictive analytics, including churn analysis, to impact both client retention and top-line growth.
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You will ensure data consistency and reliability through data cleansing and mining methodologies.
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This profile requires a proactive and detail-oriented mindset that can translate raw data into actionable strategies aligned with the company’s broader objectives.
Qualifications:
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Basic knowledge of data analytics principles and tools is required, especially Excel, including pivot tables and chart creation.
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Exposure to BI tools like Power BI, Tableau, or Looker is a plus.
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Experience or coursework related to SQL and relational databases is beneficial.
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Understanding of KPIs and their role in business monitoring and optimization is useful.
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Some experience with data integration processes or familiarity with tools like Alteryx, Apache NiFi, or Python-based ETL pipelines is desirable.
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Candidates should demonstrate the ability to perform exploratory data analysis (EDA) and communicate findings with clarity.
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You should have a curious mindset with the ability to ask business-relevant questions and pursue answers independently.
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Attention to detail is critical, especially when handling large datasets or conducting predictive modeling.
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Strong collaboration skills are needed to work across teams and translate technical findings into simple, strategic insights.
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Familiarity with churn analysis, customer segmentation, or basic machine learning techniques (regression, clustering) will be advantageous but is not mandatory.
Additional info:
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The job is based in Gurgaon, India, and requires physical presence at the office.
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It is a full-time position with no mention of contract limitations, suggesting a permanent opportunity.
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This role is suited for candidates who are in the early stages of their analytics career or those looking to transition into the field.
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Applicants with up to 3 years of experience will be considered; freshers are welcome to apply.
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The selected candidate will have the opportunity to contribute to high-impact projects supporting global operations.
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Cushman & Wakefield offers a collaborative work culture, emphasizing mentorship and skill development.
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Exposure to real estate data analytics will be a distinctive aspect of this role.
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You will learn how data drives key business functions, especially in property valuation, client management, and operational efficiencies.
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The team environment encourages regular knowledge-sharing, project collaboration, and cross-functional engagement.
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You’ll also gain practical experience in creating insights that influence business direction and client solutions.
Detailed Role Responsibilities
Business Collaboration and Stakeholder Alignment
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Act as a liaison between the data team and business units by translating stakeholder requirements into measurable data strategies.
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Regularly meet with program managers, department heads, and other key stakeholders to identify analytics gaps and align goals.
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Support decision-making by identifying which metrics directly relate to business performance and designing corresponding KPIs.
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Maintain an understanding of business goals and adapt analytics delivery to meet evolving needs.
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Provide customized reporting and insights on a regular basis, tailoring views depending on the audience (executive, operational, technical).
Data Mining and Exploration
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Perform exploratory data analysis (EDA) to understand trends, distributions, and anomalies within datasets.
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Use advanced filtering and transformation techniques to clean raw data and prepare it for further analysis.
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Mine structured and unstructured data from internal databases as well as external sources to identify business trends.
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Assist in connecting data from different departments for holistic, cross-functional views that support better strategic planning.
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Build dashboards that allow real-time data exploration and pattern recognition.
Predictive Modeling and Advanced Analytics
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Design and develop predictive models to identify potential opportunities or risks across the customer lifecycle.
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Implement churn prediction models using customer usage patterns and other behavioral indicators.
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Explore regression models to understand the factors driving revenue, cost, or service quality metrics.
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Use clustering techniques to segment customer or project data into meaningful categories for improved targeting.
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Collaborate with data scientists or consultants, if required, to validate model outputs and recommendations.
Reporting and Visualization
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Create intuitive dashboards and static reports using tools like Excel, Tableau, or Power BI.
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Present visualizations that combine multiple variables and KPIs into digestible, business-friendly formats.
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Automate recurring reports for various business teams, including sales, finance, HR, and operations.
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Highlight actionable insights using heatmaps, bar charts, waterfall diagrams, and other appropriate techniques.
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Continuously improve reporting mechanisms based on user feedback or changes in business needs.
Data Infrastructure and Integration
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Acquire data from primary and secondary sources such as ERP systems, APIs, Excel sheets, and web-based forms.
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Write SQL queries or use no-code tools to pull relevant data sets from various systems.
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Ensure data consistency across platforms by applying normalization techniques and validation rules.
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Support efforts to integrate new data sources into the analytics framework, especially as new tools or departments come online.
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Work with IT or infrastructure teams to ensure database security, version control, and compliance.
Technical Expertise and Tools
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Maintain proficiency in spreadsheet tools like Microsoft Excel or Google Sheets, including functions, pivot tables, and advanced formulas.
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Use scripting (e.g., SQL or Python) to perform ad hoc analysis or automate recurring data tasks.
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Understand how different data storage structures (relational, NoSQL, cloud) affect data querying and analysis.
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Participate in workshops or training to improve technical knowledge in analytics and data handling.
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Identify opportunities to reduce manual work through automation and smarter data structuring.
Impact on Business Performance
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Analyze operational bottlenecks and recommend improvements to increase efficiency.
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Identify potential cross-selling or upselling opportunities by analyzing client usage patterns and account behaviors.
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Measure the success of marketing campaigns, product launches, or customer success initiatives through historical data.
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Track and optimize workforce performance or departmental efficiency using time-series and comparative data.
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Recommend changes in business processes based on data trends and correlations observed over time.
Please click here to apply.
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