Solutionara Private Limited 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.
Location: Noida, India (on-site). Experience: 2–5 years. Employment: full-time.
About Solutionara
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.
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.
Required Qualifications
2–5 years of experience as a Data Analyst, BI Analyst, Data Quality Analyst, Business Data Analyst, or similar role.
Strong hands-on SQL skills.
Experience analyzing and reconciling data from multiple sources.
Experience with data profiling, validation, cleansing, or data-quality analysis.
Hands-on experience with Power BI or comparable BI/reporting tools.
Understanding of relational databases and data relationships.
Experience developing reports, dashboards, KPIs, and analytical outputs.
Strong analytical and problem-solving abilities.
Ability to investigate discrepancies and perform root-cause analysis.
Strong written and verbal communication skills.
Ability to work effectively with both technical teams and business stakeholders.
Preferred Qualifications
Experience with Power BI, DAX, and Power Query.
Working knowledge of Python for data analysis.
Exposure to cloud data platforms such as AWS, Azure, Databricks, Snowflake, Redshift, or Synapse.
Experience analyzing ERP, CRM, PIM, PLM, MES, or other enterprise application data.
Experience working with product, material, supplier/vendor, customer, inventory, location, or reference data.
Exposure to manufacturing, retail, distribution, or consumer-goods environments.
Experience supporting data migration, system integration, or enterprise transformation initiatives.
Previous experience in an IT consulting or technology-services environment.
What Success Looks Like
Data discrepancies are identified, investigated, and clearly explained.
Business and technical teams have confidence in the accuracy of analytical outputs.
Data-quality issues are detected before they become downstream reporting or operational problems.
Reports and dashboards are based on validated data and clearly defined business rules.
Data Engineers receive clear, actionable information about data-quality and transformation requirements.
You understand the business meaning behind the data rather than treating analysis as only a technical exercise.