Senior Associate - Data Analytics

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 successful candidate will work closely with business stakeholders, analytics professionals, data engineers, and technology teams to transform complex, high-volume data into trusted reporting products, actionable insights, and decision-ready recommendations.

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.

The ideal candidate will not only build technically robust dashboards and data solutions but will also interpret the underlying data, explain business performance, identify risks and opportunities, and communicate meaningful insights to business and leadership stakeholders.

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 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

Establish and apply standards for dashboard design, navigation, accessibility, visualization, and user experience

Support workspace management, content organization, report distribution, and controlled access to analytics products

Monitor data refreshes, capacity utilization, report performance, and production issues

Promote reuse of certified and governed semantic models to minimize duplicate reporting logic

Support self-service analytics by developing trusted datasets, reusable measures, templates, and user guidance

2 Microsoft Fabric development

Design, develop, and maintain analytics solutions using the Microsoft Fabric ecosystem

Build and support solutions using:

o Microsoft Fabric Lakehouse

o Fabric Data Warehouse

o Data Factory Pipelines

o Dataflows Gen2

o Fabric Notebooks

o OneLake

o Semantic Models

o 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

Diagnose and resolve failures across pipelines, notebooks, warehouses, lakehouses, dataflows, semantic models, and reports

Optimize Fabric workloads for processing efficiency, refresh performance, reliability, scalability, and responsible capacity usage

Support source-to-target mapping, technical design, lineage documentation, and dependency management

Contribute to platform standards for naming conventions, workspace structure, deployment, security, and operational support

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

Evaluate the accuracy, explainability, limitations, and business relevance of analytical outputs

Develop proactive monitoring indicators and exception-based reporting that help stakeholders focus on material changes

Quantify the potential or realized impact of identified opportunities and recommendations when supported by the available data

Track whether implemented recommendations produced the intended business outcome

Maintain clear separation between facts, analytical interpretations, assumptions, and recommendations

Ensure insights are reproducible, supported by trusted data, and communicated with appropriate business context

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

Recommend the most appropriate visualization or communication method based on the business question

Support analytics adoption by explaining how dashboards, KPIs, filters, and analytical outputs should be interpreted

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

Define calculation logic, reporting grain, dimensional attributes, filters, hierarchies, refresh expectations, and historical requirements

Assess the feasibility, complexity, data availability, dependencies, and downstream impact of new requirements

Maintain requirement traceability from business need through data, transformation logic, semantic model, report, and final validation

Manage enhancements through an agreed intake, prioritization, change-control, testing, and release process

Conduct stakeholder demonstrations and incorporate structured feedback

Support user acceptance testing by developing test scenarios, expected outcomes, and issue-resolution tracking

Build trusted relationships with business teams by providing analytical guidance rather than functioning only as a report-development resource

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

Document incidents, resolutions, preventive actions, and known limitations

Coordinate with business and technical stakeholders during releases and production issue resolution

Identify opportunities to automate validation, monitoring, ing, deployment, and operational support

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

Evaluate new capabilities based on business value, technical fit, maintainability, security, and governance implications

Promote a culture of analytical curiosity, data quality, accountability, documentation, and continuous learning

Required qualifications:

1. Education

a. Bachelor s degree in Data Analytics, Data Science, Computer Science, Information Technology, Engineering, Statistics, Mathematics, Economics, or another relevant quantitative discipline

b. An equivalent combination of relevant education and professional experience may also be considered

2. 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 developing and supporting production-grade reporting or analytics solutions
6. Demonstrated experience translating business requirements into technical and analytical deliverables
7. Experience presenting insights and recommendations to business stakeholders
8. Experience working in an Agile, iterative, or structured analytics delivery environment

Preferred qualifications:

a. Experience in marketing analytics, digital analytics, customer analytics, campaign analytics, commercial analytics, or revenue analytics

b. Experience integrating data from CRM, marketing automation, web analytics, event, finance, or enterprise operational platforms

c. Experience with Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, or related Azure services

d. Exposure to REST APIs and API-based data integration

e. Exposure to machine learning, predictive analytics, natural-language insights, or AI-assisted analytics

f. Experience with statistical analysis and hypothesis testing

g. Familiarity with data observability, metadata management, data catalogs, or lineage tools

h. Experience developing executive scorecards and leadership-facing business review materials

i. Familiarity with CI/CD, deployment pipelines, automated testing, and DevOps practices for analytics

j. Knowledge of Tableau, Qlik, Alteryx, or other business intelligence and data-preparation platforms

Business and Professional competencies:

a. Strong analytical thinking and structured problem-solving ability

b. Ability to connect data findings with business context and strategic objectives

c. Strong business acumen and curiosity about the reasons behind performance outcomes

d. Ability to independently manage analytical assignments from requirement discovery through delivery

e. Strong written, verbal, and visual communication skills

f. Ability to simplify complex technical and analytical concepts for non-technical audiences

g. Strong attention to detail and commitment to data accuracy

h. Ability to prioritize multiple assignments and manage stakeholder expectations

i. Ability to collaborate across business, analytics, engineering, and technology teams

j. Ability to constructively challenge assumptions and validate business interpretations

k. Strong documentation, organization, and knowledge-sharing skills

l. Ownership mindset with a focus on quality, reliability, delivery, and measurable outcomes

Required technical skills:

Competency | Expected Proficiency

PowerBI - Advanced report development, semantic modeling, visualization, publishing, security, and performance optimization
DAX - DAX Advanced measures, filter context, time intelligence, calculation logic, and optimization
Power Query - Data ingestion, cleansing, transformation, parameterization, and reusable query design
Microsoft Fabric - Working knowledge of Lakehouse, Warehouse, Pipelines, Dataflows Gen2, Notebooks, OneLake, and Semantic Models
SQL - Advanced querying, joins, CTEs, window functions, transformation, aggregation, validation, and performance optimization
Python - Data analysis, automation, data manipulation, and analytical workflows
Data Modeling - Dimensional modeling, star schemas, snowflake schemas, fact and dimension design, and semantic modeling
ETL / ELT - Pipeline development, incremental processing, validation, logging, exception handling, and monitoring
Business Intelligence - KPI design, enterprise reporting, self-service analytics, executive dashboards, and governed datasets
Advanced Analytics - Exploratory analysis, root-cause analysis, segmentation, forecasting, and basic predictive modeling
Data Governance - Data quality, lineage, metadata, documentation, access control, and certified data products
Version control - Git or equivalent source-control practices
Delivery tools - Azure DevOps, Jira, or comparable work-management platforms

Preferred certifications:

• Microsoft Certified: Power BI Data Analyst Associate, PL-300
• Microsoft Certified: Fabric Analytics Engineer Associate, DP-600
• Microsoft Certified: Fabric Data Engineer Associate, where relevant to the role
• Microsoft Azure Data Engineer or another relevant Azure data certification
• Alteryx Designer certification, if applicable to the team s technology landscape

Indicative success measures:

Success in this role may be assessed through:

• Accuracy, completeness, and reliability of analytics and reporting solutions
• Reconciliation between approved source data and business intelligence outputs
• Performance and stability of Power BI reports, semantic models, pipelines, and Fabric workloads
• Timely delivery of analytics products and enhancements against agreed requirements
• Reduction in manual reporting and recurring analytical effort
• Adoption and effective utilization of dashboards and analytical products
• Quality and relevance of insights and recommendations delivered to stakeholders
• Demonstrated contribution of insights to business decisions, optimization actions, or risk identification
• Compliance with data governance, security, testing, documentation, and release standards
• Reusability of data products, semantic models, measures, transformation components, and analytical frameworks
• Effectiveness of production monitoring, incident resolution, and preventive improvements
• Stakeholder satisfaction with analytics delivery, communication, and business partnership

Core competency mix:

Competency Area | Indicative Focus
Power BI and Business Intelligence Development - 30%
Microsoft Fabric and Data Engineering - 20%
Insight Generation and Advanced Analytics - 25%
Stakeholder Engagement and Data Storytelling - 15%
Data Governance, Quality, Testing, and Documentation - 10%

Ideal candidate profile:

The ideal candidate is a well-rounded analytics professional who combines business intelligence expertise, Power BI development, Microsoft Fabric engineering, and business insight generation.

The candidate should be capable of independently converting a business problem into a complete analytics solution, including identifying the required data, developing transformation logic, building governed analytical models, creating intuitive dashboards, validating results, interpreting performance, and presenting actionable recommendations.

The individual should demonstrate the ability to operate as both a technical analytics practitioner and a business-facing insight partner. Success in the role requires more than producing reports. It requires the ability to establish trust in the data, explain what is driving business outcomes, identify relevant opportunities and risks, and help stakeholders make informed decisions.