Data Analyst

The Retail Data Analyst will provide data services to stakeholders within the point-of-sale project implementation by managing technical data aspects and data resources supporting data integrations, data conversions, reporting, business intelligence, analytics, and operational applications. They engage with data-centric project managers, business analysts, developers, technology teams, and project stakeholders to understand capability requirements and define and construct data structures and solutions based on priorities. We value diversity in the workplace and encourage women, minorities, and veterans to apply. Thank you!

Type: Contract

Location: Remote Travel (1 week onsite per month in Bend, OR).

Hours: PST working hours

More about this role:

The Retail Data Analyst performs the design, development, and operational tasks required to provide accurate, timely, high-quality data critical to feed data products (i.e., integrations, data conversions, reports, and analyses) and support downstream applications.

This role will leverage appropriate data analysis tools/solutions, have a strong business acumen and an ability to quickly learn and comprehend new concepts. This position requires a moderate level of supervision delivering outcomes, moderate level of breadth/depth of job-specific knowledge, and a mature level of service delivery experience, professionalism, and communication skills. The consultant will report to the Technical Project Manager.

Primary Responsibilities/Functions:

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions/primary responsibilities.

60%: Data Structure and Solution Design

• Collaborate with business systems analysts, data analysts, business stakeholders, and analytics practitioners to understand data product and downstream system data requirements

• Collaborate with data stewards and data source managers to understand data definitions and business rules relevant to data structure design

• Perform detailed design of data structures from inception through production support

• Create data models for relational and dimensional database schemas for a range of use cases from targeted reporting solutions to support downstream applications

• Conduct information modeling, including conceptual models, relational database designs and message models

• Design and implement data manipulation, preparation, and data movement processes for a variety of scenarios from simple file-based export/import to enterprise-grade ETL workflows connecting multiple structured and unstructured endpoints

• Promote and provide leadership implementing publish-subscribe integration utilizing canonical message format principals

• Manage activities of Technical Group Data Team members through the creation and management of the data plan aspects of the Technical Pathway Plan

40%: Data Platform Quality and Governance

• Follow a quality assurance framework to ensure the delivery of high quality data and data structure analyses to stakeholders

• Use development best practices, version control, and deployment strategies to ensure product quality, agility, and recoverability

• Implement and test data access roles and permissions to ensure “least privileged” access to enterprise data and reduce the enterprise risk of data exposure

• Maintain and continuously improve data work flows and query performance

• Ensure understanding of data strategy and data plan progress across business systems analysts, data analysts, business stakeholders, analytics practitioners, data stewards and data source managers

Educational/Experience:

• Bachelor's degree (BS or BA) in a STEM-related discipline with a major in Computer Science/Information Management/Database Development and Analysis or equivalent disciplines, or equivalent experience with appropriate time-in-role.

• 4 years of experience with data warehouse technical architectures, ETL/ELT, reporting/analytic tools and scripting.

• Experience with AWS services including S3, data pipeline, and cloud-based data warehouses.

• Experience with data visualization tools such as Power BI, Fabric, Tableau or Birst is a plus.

• Experience utilizing planning tools such as Smartsheets, Jira and Service Now

Technical Skills/Knowledge:

• Advanced data modeling skills

• Proficient in the composition of Advanced SQL (analytical functions)

• Proficient in query performance tuning

• Proficient in Data Analysis techniques for testing and troubleshooting

• Knowledge of ETL process development

• Proficiency in writing and maintaining data management documentation such as data dictionaries, data catalogs, and integration data maps

• Procedure or function development in T-SQL, Oracle PL/SQL, or equivalent

• Proficiency and demonstrated professional experience working with flat file data formats including delimited files, XML, and JSON

• Practical experience using solution delivery collaboration software such as ServiceNow, Jira, TFS, or similar

• Strong technical skills including acquiring new datasets, parsing datasets, filtering and organizing data, mining data for patterns, representing data visually, machine-learning algorithms and automating data-driven models; adept at knowledge discovery and data inference and exploration, as well as predictive and prescriptive analytics approaches; experience with types of technologies from big data tools to advanced analytics platforms and programming languages; mathematical analysis skills that include regression analysis, data sampling, experimentation (hypothesis) design, correlation and conjoint analysis.

General Knowledge and Abilities:

Ability to design data analysis approaches to meet target outcomes using

best-practice data science techniques; ability to effectively represent and convey the essence of complex concepts and solutions in simple, understandable ways to wide audiences; ability to complete and present insights to leadership and stakeholders using sophisticated analysis techniques; ability to collaborate with data management and data stewards on data quality issues that challenge data analysis efforts; ability to complete fact-based data analysis as part of business case development; ability to create business algorithms/models by understanding underlying relationships