Data Analyst

Job Description:

• The Data Analyst will be a key part of the Enterprise Data Services team, which is responsible for transforming data from disparate systems to provide insights and analytics for business stakeholders.
• Specifically, this role will support a multi-year project to migrate legacy data jobs and reports owned by business areas across the company to cloud-based technology solutions and modern data tools.
• You will collaborate with Data Engineers, Data Analysts, DBAs, cross-functional teams, and business teams.
• You will analyze, classify, migrate or redesign data jobs/reports into modern data tools, using Agile methodology, that empower users to make informed business decisions. Progress will be documented and validated against current report outputs.
• *This is an entry-level position, and we welcome applications from recent graduates or individuals with limited data analysis experience.*
• You are self-motivated, work independently, and have a strong desire to gain experience in data tools, data analysis, data engineering, data consulting, etc. Strong candidates will exhibit critical thinking skills, the ability to break down technical problems, and a desire to help transforming data to create solutions that add value to business requirements.

Requirement:

• • Bachelor of Science degree in Computer Science or equivalent • At least some post-degree professional experience
•  Strongly preferred experience in data analysis to create, publish, and/or manage reports and data visualizations in Tableau, PowerBI, AWS Quicksight, or AWS DataZone (i.e., our target solutions)
• Knowledge of the best practices in building and preparing data for analytics, including data prep, semantic data models
•  Strong knowledge with database technologies and data development such as Python, PLSQL, etc.
• Understanding how to build and modify data queries/applications, including performance tuning
• Identify necessary business rules for extracting data along with functional or technical risks related to data sources (e.g. data latency, frequency, etc.)
•  Basic understanding of performing test cases for profiling data, validating analysis, testing assumptions, driving data quality assessment specifications, and define a path to deployment
• Comprehension of best practices for data ingestion and data design • Familiar with best practices for data ingestion and data design