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United Parcel Service

Ships packages and freight around the world

Data and Analytics Specialist - Digital Intelligence

Data Engineering +1Confirmed live in the last 24 hours
Full-timeSingaporeNo salary listedIn-person

About the Job

Requirements

  • A Bachelor’s Degree in Business Analytics, Data Analytics, Marketing, Data Science, Information Systems, Computer Science, Statistics, Engineering, or a related discipline is required.
  • 2–5 years of relevant experience in data analytics, business intelligence, data engineering, marketing analytics, or a related function; candidates with strong hands-on project experience may also be considered.
  • Strong hands-on experience with Power BI and Power Query, including data modeling and dashboard/report development.
  • Strong SQL skills and experience working with relational databases, enterprise data warehouses, or large transactional datasets.
  • Sound understanding of data modeling, ETL/data preparation, data validation, and combining multiple data sources at different levels of detail.
  • Strong analytical and structured problem-solving skills, with the ability to translate business questions into practical data and BI solutions.
  • Effective communication skills, with the ability to present technical insights to business stakeholders.
  • Attention to detail and ability to manage multiple priorities in a fast-paced, matrixed, regional environment.
  • Collaborative, proactive, adaptable, and willing to learn, with a strong sense of ownership and accountability.

Responsibilities

  • Translate customer and marketing business requirements into scalable data models, datasets, and analytical solutions supporting the APAC Single View of Customer.
  • Integrate customer/account information, shipment and revenue performance, product or service usage, customer segmentation, and marketing engagement data into reusable customer-level analytical structures.
  • Support development of relationships between UPS ID holders and account holders where data and identifiers allow, and enable analysis across relevant customer populations and sales coverage segments.
  • Develop approaches to retain and analyze customer history over time, including changes that may no longer be visible in current monthly source files.
  • Partner with Customer and Campaign Intelligence stakeholders to implement agreed customer lifecycle, campaign, and KPI definitions in analytical models and reporting solutions.
  • Extract, combine, transform, and validate data from enterprise data warehouses, CRM/marketing platforms, and other approved data sources using Power Query, SQL, and other appropriate tools.
  • Develop repeatable ETL and data preparation workflows that reduce manual effort, improve consistency, and support recurring customer, campaign, and performance analytics.
  • Design and maintain reusable Power BI semantic models, DAX measures, and reporting solutions rather than isolated one-off dashboards.
  • Improve data quality, refresh reliability, model performance, and documentation, including source-to-report logic, transformations, dependencies, and validation checks.
  • Support the prioritized migration of Microsoft Access and spreadsheet-based analytical processes toward more scalable and maintainable solutions.
  • Use Python where data scale, complexity, automation, or analytical requirements make it appropriate.
  • Enable standardized campaign analytics, customer segmentation, lifecycle analysis, reactivation, cross-sell, and customer growth analysis through reliable datasets and analytical tools.
  • Support customer-level analytics for strategic programs, including preparation of analytical views and measurement datasets.
  • Progressively support advanced analytical use cases such as clustering, propensity analysis, at-risk identification, experimentation, and recommendation analytics as the data foundation matures.
  • Provide technical guidance and reusable components that enable other Digital Intelligence team members to develop and maintain their own Power BI reporting and analysis.
  • Partner with regional, country, Corporate, IT, and external vendor stakeholders as required to support scalable analytics solutions and productionization.

Desired Qualifications

  • Experience with Python for data manipulation, automation, or analytics is strongly preferred.
  • Familiarity with DAX, CRM/marketing data, Oracle, historical/snapshot data, or campaign measurement is an advantage.
  • Portfolio or dashboard samples are highly encouraged.

About the Company

United Parcel Service

Ships packages and freight around the world

United Parcel Service picks up packages from businesses and households and delivers them by truck and plane to addresses around the world. Beyond small parcels, it moves heavy freight, clears imported goods through customs, and runs warehouses and distribution for other companies. Each package is scanned as it passes through local centers and regional sorting hubs, which lets senders and recipients track it from pickup to doorstep. Customers pay shipping rates set by package weight, size, distance, and speed, while large shippers negotiate their own contract rates. UPS was founded in 1907 in Seattle as a messenger service and is based in Atlanta, Georgia.

Company size
10,001+
Company stage
IPO
Headquarters
Atlanta, GA
Founded
1907

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