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:
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 dynamic 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:
Bachelor’s degree in Data Analytics, Data Science, Computer Science, Information Technology, Engineering, Statistics, Mathematics, Economics, or another relevant quantitative discipline
An equivalent combination of relevant education and professional experience may also be considered
Professional experience:
4–7 years of experience in business intelligence, data analytics, data engineering, reporting, or a related discipline
At least 3 years of hands-on experience developing Power BI reports, dashboards, semantic models, and analytical solutions
Hands-on experience working with Microsoft Fabric or a comparable contemporary cloud analytics platform
Experience working with large, complex, and multi-source datasets
Experience working in an Agile, iterative, or structured analytics delivery environment
Experience in marketing analytics, digital analytics, customer analytics, campaign analytics, commercial analytics, or revenue analytics
Experience with Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, or related Azure services
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

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