Job summary:

The Senior Associate – Data Analytics, Business Intelligence & Microsoft Fabric will be responsible for designing, developing, and supporting scalable analytics and business intelligence solutions using Microsoft Power BI and Microsoft Fabric.

The role combines business intelligence development, big data processing, data modeling, visualization, advanced analytics, and insight generation.

The candidate will be expected to manage analytics deliverables across the full lifecycle, including requirements analysis, data acquisition, transformation, modeling, visualization, validation, deployment, monitoring, and continuous enhancement. The role will also contribute to data governance, documentation, platform optimization, self-service analytics, and the development of reusable enterprise data products.

Location: Hyderabad/Kolkata, India.

Key responsibilities:

1. Business Intelligence and PowerBI development

- Design, develop, deploy, and maintain interactive Power BI dashboards, reports, scorecards, and analytical applications
- Translate business requirements and analytical questions into scalable and intuitive business intelligence solutions
- Develop enterprise-grade semantic models that support consistent KPI definitions, reusable calculations, and self-service reporting
- Create advanced DAX measures, calculated tables, calculated columns, time-intelligence calculations, and agile business metrics
- Use Power Query and M to ingest, cleanse, transform, and prepare data from multiple sources
- Apply dimensional modeling principles, including star and snowflake schemas, to develop efficient and scalable reporting models
- Design executive, operational, and analytical dashboards that clearly communicate performance, trends, risks, and opportunities
- Implement drill-through, bookmarks, tooltips, row-level security, object-level security, and other relevant Power BI capabilities
- Optimize data models, DAX calculations, visuals, report interactions, and refresh processes to improve performance and usability

2 Microsoft Fabric development

- Design, develop, and maintain analytics solutions using the Microsoft Fabric ecosystem
- Build and support solutions using:
- Microsoft Fabric Lakehouse
- Fabric Data Warehouse
- Data Factory Pipelines
- Dataflows Gen2
- Fabric Notebooks
- OneLake
- Semantic Models
- Power BI
- Develop scalable ETL and ELT processes to acquire, transform, integrate, and publish data for analytics consumption
- Create pipelines and notebooks for batch-based data ingestion and transformation
- Use SQL, Python, and PySpark to process structured and semi-structured datasets
- Develop curated data layers that support reporting, analytics, and downstream business use cases
- Contribute to the implementation of medallion or similar layered data architecture patterns, where applicable
- Support development, testing, deployment, and production monitoring across analytics environments
- Implement parameterization, configuration management, logging, reconciliation, exception handling, and restart capabilities in data workflows

3 Insight generation and Advanced Analytics

- Analyze business, marketing, customer, digital, campaign, operational, and performance data to identify trends, patterns, risks, anomalies, and growth opportunities
- Move beyond reporting what happened by investigating why it happened, what it means, and what action should be considered
- Generate actionable insights that support strategic, tactical, and operational decision-making




- Conduct exploratory data analysis, root-cause analysis, variance analysis, trend analysis, segmentation, cohort analysis, and performance-driver analysis
- Interpret relationships across multiple datasets and connect analytical findings to business outcomes
- Develop hypotheses, define analytical approaches, validate findings, and present evidence-based conclusions
- Identify underperformance, outliers, emerging trends, measurement gaps, and optimization opportunities
- Develop analytical views that differentiate signals from routine fluctuations
- Support forecasting, propensity analysis, classification, clustering, predictive analytics, and other advanced analytical use cases, where appropriate

4 Data storytelling and Executive communication

- Convert complex analysis into clear, concise, and compelling business narratives
- Create executive-ready presentations, insight summaries, performance commentary, and business review materials
- Communicate not only data points but also the business context, contributing factors, implications, and recommended actions
- Tailor analytical communication to different audiences, including business users, technical teams, functional leaders, and senior leadership
- Present analytical findings with a logical storyline supported by appropriate visualizations
- Clearly articulate data limitations, assumptions, dependencies, risks, and confidence levels
- Develop commentary that explains the most material changes in KPIs and performance drivers
- Facilitate business reviews and analytical discussions, helping stakeholders interpret and act on the findings

5 Business partnership and requirements management

- Partner with business stakeholders to understand strategic priorities, operational processes, reporting requirements, and analytical questions
- Lead or support requirements-gathering discussions, discovery workshops, design reviews, demonstrations, and user-acceptance sessions
- Translate business needs into documented functional requirements, analytical requirements, data requirements, KPIs, and acceptance criteria
- Challenge ambiguous requests and help stakeholders define the decisions or actions that the analytics solution should support
- Conduct stakeholder demonstrations and incorporate structured feedback
- Support user acceptance testing by developing test scenarios, expected outcomes, and issue-resolution tracking

6 Data Quality, Governance, Security, and Controls

- Embed data quality controls within ingestion, transformation, modeling, and reporting workflows
- Validate that reporting outputs reconcile with approved source systems and documented business rules
- Define and monitor relevant data-quality measures and thresholds
- Investigate data-quality exceptions and coordinate remediation with source-system owners and data teams
- Maintain business definitions, KPI logic, data dictionaries, source-to-target mappings, calculation specifications, and technical documentation
- Support data lineage and metadata-management practices across analytics assets
- Apply appropriate role-based access, row-level security, workspace permissions, and data-classification requirements




- Ensure that analytics products comply with applicable organizational security, privacy, retention, and governance standards
- Promote consistent business definitions and a single source of truth across dashboards and reports
- Prevent the uncontrolled duplication of measures, datasets, transformation logic, and reporting products
- Maintain auditability of transformations, calculations, changes, releases, and validation results
- Participate in peer reviews, design reviews, release controls, and production-readiness assessments
- Identify governance gaps and recommend improvements to increase trust, transparency, and maintainability

7 Testing, Deployment, and Production Support

- Develop and execute unit testing, integration testing, data validation, regression testing, and performance testing
- Validate data across source, transformed, curated, semantic, and visualization layers
- Prepare test evidence and resolve defects before production release
- Support controlled deployments across development, testing, and production environments
- Use version control and deployment practices to maintain consistency and traceability
- Monitor scheduled refreshes, pipeline execution, semantic model processing, and dashboard availability
- Troubleshoot production issues and perform root-cause analysis

8 Continuous improvement and Team contribution

- Identify opportunities to automate manual reporting, repetitive transformations, reconciliations, and quality checks
- Recommend improvements to analytics architecture, development standards, delivery processes, and governance controls
- Create reusable templates, code components, calculation libraries, documentation standards, and development accelerators
- Conduct peer reviews for Power BI reports, semantic models, SQL scripts, notebooks, and pipelines
- Mentor associates and junior team members in analytics, Power BI, Microsoft Fabric, data modeling, and insight-generation practices
- Contribute to team onboarding, knowledge-sharing sessions, technical documentation, and capability development
- Stay informed about relevant developments across Microsoft Fabric, Power BI, cloud analytics, artificial intelligence, and business intelligence

Education:

1. Bachelor’s degree in Data Analytics, Data Science, Computer Science, Information Technology, Engineering, Statistics, Mathematics, Economics, or another relevant quantitative discipline
2. An equivalent combination of relevant education and professional experience may also be considered

Professional experience:

1. 4–7 years of experience in business intelligence, data analytics, data engineering, reporting, or a related discipline
2. At least 3 years of hands-on experience developing Power BI reports, dashboards, semantic models, and analytical solutions
3. Hands-on experience working with Microsoft Fabric or a comparable modern cloud analytics platform
4. Experience working with large, complex, and multi-source datasets
5. Experience working in an Agile, iterative, or structured analytics delivery environment
6. Experience in marketing analytics, digital analytics, customer analytics, campaign analytics, commercial analytics, or revenue analytics
7. Experience with Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, or related Azure services
8. Familiarity with CI/CD, deployment pipelines, automated testing, and DevOps practices for analytics. Knowledge of Tableau, Qlik, Alteryx, or other business intelligence and data-preparation

📌 Senior Associate - Data Analytics (Hyderabad)
🏢 Grant Thornton INDUS
📍 Hyderabad