About the Company
Solutionara is an AI-led IT Consulting and Technology Services organization delivering enterprise transformation through Cloud, Integration, Data Engineering, Digital Experience, AI, and Enterprise Applications. Headquartered in the United States with a Global Delivery Center (GDC) in Noida, India, Solutionara partners with enterprise organizations across retail, manufacturing, distribution, consumer goods, and other industries to design, implement, and support scalable technology solutions. As a rapidly growing consulting organization, we foster a culture of ownership, innovation, collaboration, and continuous improvement, empowering our teams to contribute beyond their immediate responsibilities while delivering exceptional value to our global clients. Every team member is expected to think like a consultant and business problem solver—understanding client objectives, challenging assumptions when appropriate, leveraging AI responsibly, and continuously identifying opportunities to improve quality, productivity, and customer outcomes. AI is embedded into how we work, but it is an enabler—not a replacement for expertise, critical thinking, or accountability.
Role Overview
Solutionara is seeking a hands-on Data Analyst – BI & Data Quality to work across enterprise data analysis, data quality, reconciliation, reporting, and business intelligence. The role requires someone who can go beyond creating reports and dashboards. You will work directly with complex enterprise datasets, investigate data discrepancies, understand business rules, perform source-to-target validation, identify data-quality issues, and convert trusted data into meaningful reporting and business insights. You will collaborate closely with Data Engineers, technical leads, and business stakeholders to ensure that data is accurate, understandable, and fit for business use.
Key Responsibilities
Data Analysis
- Analyze large and complex datasets using SQL and analytical tools.
- Perform exploratory analysis to understand data structures, relationships, patterns, and anomalies.
- Understand business definitions and determine how they are represented across source systems.
- Analyze data across multiple enterprise systems and identify inconsistencies.
- Support source-to-target data mapping and validation. Perform reconciliation between source and target datasets.
- Investigate data discrepancies and support root-cause analysis.
- Translate business questions into structured data analysis.
Data Quality
- Profile datasets to assess completeness, accuracy, consistency, validity, and uniqueness.
- Identify duplicate, missing, invalid, and inconsistent records.
- Help define and document business and data-quality rules.
- Execute data-quality checks and validate remediation results.
- Analyze exceptions and determine patterns or root causes.
- Work with Data Engineers to translate identified quality requirements into automated controls.
- Maintain data-quality metrics and exception reporting.
- Support validation during data migration and integration activities.
BI & Reporting
- Design and develop dashboards and reports using Power BI or equivalent BI technologies.
- Develop reporting datasets and semantic models.
- Create KPIs, measures, calculations, and business logic.
- Build operational, analytical, reconciliation, and data-quality dashboards.
- Validate reports against underlying source data. Identify trends and communicate meaningful findings to stakeholders.
- Optimize reports for usability, performance, and clarity.
Business & Client Collaboration
- Work with business and technical stakeholders to understand data and reporting requirements.
- Ask appropriate questions to understand the business purpose behind requested analysis.
- Document business definitions, data rules, mappings, assumptions, and findings.
- Communicate data-quality issues and analytical findings clearly to technical and non-technical audiences.
- Participate in client meetings, requirement discussions, and data-review sessions as required.
- Proactively identify data issues, risks, and opportunities for improvement.
AI-Led Working
- Use AI-assisted tools responsibly to accelerate data analysis, SQL development, documentation, validation, and reporting.
- Use AI to assist with pattern identification and investigation while independently validating conclusions.
- Identify opportunities to automate repetitive analysis and reporting activities.
- Apply business context and critical thinking before accepting AI-generated results or recommendations.
Tech Stack
- Data Analysis Tools: SQL, Python (preferred)
- Power BI: DAX, Power Query
- Cloud Data Platforms: AWS, Azure, Databricks, Snowflake, Redshift, Synapse
- Enterprise Applications (data from): ERP, CRM, PIM, PLM, MES
- Data Profiling & Validation: Tools for completeness, accuracy, consistency, validity
- Data Quality Tools: Profiling, validation, cleansing, reconciliation
- BI/Reporting: Power BI (core), KPIs, measures, dashboards
- Databases: Relational databases, data relationships
- Automation: AI-assisted tools for SQL development, documentation, reporting automation