Data Analyst Business Intelligence

AssistRx

2d•Hybrid

About The Position

The Data Analyst is responsible for supporting business intelligence and analytics delivery by partnering with BI Developers, BI Architects, Data Architects, Data Engineers, and business stakeholders to translate business needs into clear, development-ready requirements. This role supports the delivery of reporting and analytics solutions by understanding where data resides, how it moves through ETL/ELT processes, how it is modeled for reporting, and how business requirements should be documented for technical delivery. The Data Analyst serves as a liaison between business users and technical teams, helping define report requirements, clarify data logic, document source-to-target mappings, create development-ready tickets, support testing, and ensure BI solutions align with enterprise data standards and architecture patterns.

Requirements

Bachelor's degree in Information Systems, Business Analytics, Computer Science, Data Analytics, Business Administration, or related field; equivalent experience may be considered.
3+ years of experience as a Data Analyst, BI Analyst, Business Analyst, Technical Business Analyst, Systems Analyst, or similar role supporting data, reporting, or analytics initiatives.
Strong understanding of BI delivery processes, reporting requirements, data models, and analytics solution design.
Experience working with BI tools such as Power BI, ThoughtSpot, Tableau, Looker, or similar platforms.
Experience working with data warehouses, relational databases, or cloud data platforms such as Snowflake, SQL Server, Azure, or similar technologies.
Ability to read and write SQL for data investigation, validation, requirements analysis, and troubleshooting.
Understanding of ETL/ELT concepts, data pipelines, source-to-target mapping, transformation logic, and data dependencies.
Experience creating user stories, acceptance criteria, process flows, data mappings, technical specifications, and supporting documentation.
Strong communication skills with the ability to work effectively between business stakeholders and technical teams.
Strong analytical, problem-solving, and organizational skills with attention to detail and accuracy.

Nice To Haves

Experience with Agile delivery tools such as Jira, Azure DevOps, or similar platforms preferred.
Preferred domain experience includes healthcare, pharmacy, patient services, specialty pharmacy, hub services, payer/insurance, life sciences, pharmaceutical, or other regulated operational reporting environments.

Responsibilities

Partner with business stakeholders to gather, clarify, and document reporting and analytics requirements.
Work closely with BI Developers, BI Architects, Data Architects, and Data Engineers to translate business needs into technical specifications.
Understand where data resides across source systems, data warehouses, semantic layers, and BI tools.
Analyze ETL/ELT processes, data flows, transformations, and dependencies to support BI solution design.
Document source-to-target mappings, business rules, metric definitions, filter logic, report logic, and reporting assumptions.
Create clear, development-ready user stories, tickets, acceptance criteria, and supporting documentation for technical teams.
Support backlog refinement, sprint planning, testing, and release coordination for BI and analytics work.
Validate reporting outputs against source data, business rules, requirements, and expected results.
Investigate data discrepancies, reporting defects, and gaps between business expectations and technical implementation.
Coordinate across BI, data architecture, data engineering, and business teams to resolve issues and drive delivery.
Support standardization of BI requirements, documentation practices, reusable reporting patterns, and intake processes.
Assist with user acceptance testing and production validation for new or enhanced dashboards, reports, datasets, and semantic models.
Communicate technical concepts clearly to non-technical stakeholders and communicate business context clearly to technical teams.
Identify opportunities to improve reporting processes, data usability, development handoffs, and operational efficiency.