Entry-Level Visualization Engineers

Are you passionate about uncovering the story behind data? Do you enjoy transforming raw data into powerful insights that guide business decisions? Are you driven to improve processes, ensure data quality, and collaborate across teams to create smarter solutions?

We are looking for a Visualization Engineer who thrives in complexity, embraces curiosity, and brings a sharp analytical mindset to everything they do. If you're ready to make an impact in a global organization that’s shaping the future of financial services, we want to hear from you.

Western Union powers your pursuit.

Key Responsibilities:

Data Analysis:

1. Gather data from various sources, clean, validate and prepare for analysis. Define data acquisition and integration/transformation logic, using appropriate methods and tools within the defined technology stack to ensure scalability of the data to meet various use cases.
2. Perform exploratory data analysis to uncover trends, patterns, anomalies and outliers.
3. Deep dive into complex data and processes, perform gap analysis, risk assessment, and apply statistical and advanced analytical techniques to derive insights from the data.
4. Conduct hypothesis testing to make inferences and support data-driven decisions.
5. Partner with Business units to define Key Performance Indicators (KPIs) and metrics.
6. Provide actionable data insights and recommendations based on data analysis findings to Business or Stakeholders through interactive dashboards, reports and presentations.
7. Identify opportunities to optimize data acquisition and analysis processes.
8. Design and implement data analysis frameworks for complex datasets.
9. Translate business requirements into technical specifications for data solutions.
10. Conduct end-to-end analysis of a data flow and technology stack to create an effective RCA on data quality/data integrity issues using a collection of principles, techniques, and methodologies to identify the root causes of an event or trend.
11. Formulate corrective actions/potential solutions backed by complete and thorough research to understand the problem, processes, and solution to eliminate underlying issues.
12. Oversee proper course of action for remediation of the issue in a timely manner.
13. Implement preventive measures to improve data integrity and data-driven decision making.
14. Investigate areas that need improvement in efficiency and productivity.
15. Communicate findings/recommendations to Data Engineering Leaders or Stakeholders of the analysis results.

Data Quality Assessment and Monitoring:

1. Perform data profiling and quality checks to evaluate the health of data scorecards (accuracy, completeness, consistency, reliability, timeliness, uniqueness, usefulness, differences).
2. Coordinate to catalog data quality rules and apply active data quality in the Data Engineering pipelines to proactively detect and address potential data issues.
3. Communicate data cleansing techniques to correct errors, standardize formats, and address data inconsistencies based on findings to Data Engineering Leaders.
4. Standardize data validation checks to be performed throughout the different stages of data lifecycle (extraction, replication, ingestion, storage, integration, transformation, cleansing, augmentation, validation, presentation) to ensure data accuracy.
5. Collaborate with cross-functional teams to design and implement frameworks to monitor and track data movement and changes across the various integrated systems.
6. Identify process improvement and optimization opportunities.

Required Skills and Qualifications:

1. Bachelor's degree in computer science, statistics, mathematics, or related field. Master’s degree preferred.
2. Knowledge in data profiling, data discovery, information chain analysis, root cause analysis, cost benefit analysis, data analysis, statistical analysis, business intelligence or related roles.
3. Understanding of data management, data governance with a focus on data quality.
4. Strong analytical skills with proficiency in data analysis tools, and programming languages (SQL, Python, R, etc.).
5. Knowledge of Data Lake, Data Warehousing, Database concepts and technologies.
6. Understanding in data extraction, transformation, and loading (ETL) processes.
7. Experience working with large and complex data sets, applications, workflows, and systems. Knowledge of network, log analytics, scripting, APIs.
8. Proficiency in data visualization tools and techniques.
9. Excellent problem-solving abilities and critical thinking with strong attention to detail.
10. Ability to effectively communicate complex data insights to both technical and non-technical audiences.