Data Analytics Specialist

Bentley University•Waltham, MA

1d•Hybrid

About The Position

Reporting to the Associate Director of Data Analytics and Insights on the Data Engineering and Operations team, the Data Analytics Specialist supports Bentley University’s enterprise data and analytics ecosystem by translating business needs into well-defined data requirements, building and maintaining trusted reporting and dashboard solutions, and partnering with data engineering and business teams to deliver scalable data products. The role combines business analysis, BI development, data modeling, testing, and hands-on engineering support across the analytics delivery lifecycle. The position may apply statistical, predictive, or machine learning techniques when they are appropriate to the business need, while maintaining a primary focus on reliable data, actionable analysis, and decision-ready reporting.

Requirements

Bachelor’s degree in data analytics, information systems, computer science, statistics, mathematics, economics, or a related field.
Two or more years of progressive experience in data analytics, business systems analysis, enterprise reporting, business intelligence, or a related discipline.
Demonstrated expertise in requirements-gathering methodologies, including stakeholder interviews, process mapping, use-case development, and workflow analysis, and in translating business requirements into detailed technical specifications for reporting, dashboards, data models, and data solutions.
Two or more years of hands-on experience with Power BI or other similar tools, including data modeling, DAX, Power Query (M), semantic model design, dashboard prototyping, iteration, deployment, and ongoing optimization across the full BI development lifecycle.
Two or more years of experience with data modeling and the design of efficient, analytics-ready data structures.
Proficiency with SQL and experience extracting, transforming, validating, and analyzing structured and semi-structured data; familiarity with unstructured data is beneficial.
Strong understanding of data warehousing concepts, relational and dimensional data models, ETL/ELT frameworks, and cloud data platform concepts, preferably within the Azure ecosystem, such as Data Lake, Synapse, Fabric, or comparable technologies.
Proven experience collaborating with data engineering teams to refine data pipelines, validate data quality, support data governance, troubleshoot data flow issues, and deliver scalable analytics solutions.
Experience planning and executing system, data, integration, and User Acceptance Testing, including issue tracking and resolution validation.
Ability to create high-quality business and technical requirements, data dictionaries, process documentation, test plans, source-to-target mappings, and user guides, with strong attention to detail and version control.
Strong analytical and problem-solving skills, with the ability to transform complex datasets into clear findings and actionable insights for technical and non-technical audiences.
Excellent communication, facilitation, and collaboration skills, with the ability to lead cross-functional discovery sessions, build alignment, and support decision-making.
Ability to manage multiple projects and priorities, work independently and collaboratively, and adapt in a dynamic environment.

Nice To Haves

Experience in higher education and familiarity with academic or administrative data domains.
Experience with Python or R for data preparation, automation, statistical analysis, or selective advanced analytics use cases.
Experience with Azure data services, Microsoft Fabric, data lakehouse or warehouse platforms, Git-based version control, and deployment workflows.
Familiarity with data privacy, security, governance, and compliance requirements associated with institutional or student data.
Experience applying predictive analytics or machine learning techniques to well-defined business problems, including evaluation and communication of model limitations.
Relevant certifications in Power BI, Microsoft Fabric, Azure, data engineering, data analytics, or related technologies.

Responsibilities

Collaborate with business partners, cross-functional teams, and IT units to gather, clarify, and document detailed business, data, and reporting requirements.
Translate business needs into technical specifications, user stories, acceptance criteria, data mappings, semantic model requirements, and analytics deliverables.
Lead or support stakeholder interviews, discovery sessions, process mapping, workflow analysis, and use-case development.
Conduct data discovery and profiling to understand source systems, data structures, data quality, lineage, and end-to-end data movement.
Research and work with the data governance manager to resolve complex questions related to enterprise reporting, data definitions, business rules, and data usage across systems.
Partner with data governance stakeholders to define shared terminology, document data definitions, and promote consistent and responsible data use.
Analyze, define, design, build, test, deploy, and maintain reports and interactive dashboards across BI platforms, including Power BI and SQL-based outputs.
Develop and maintain semantic models, relationships, calculations, queries, and measures using Power BI, DAX, Power Query (M), and SQL.
Create intuitive data visualizations that communicate insights clearly to technical and non-technical audiences and support data-informed decision-making.
Perform exploratory and statistical analysis to identify trends, patterns, relationships, and actionable insights.
Apply predictive analytics, machine learning, or other advanced analytical methods selectively when they add clear value; evaluate results and communicate limitations appropriately.
Provide dashboard prototyping, iterative refinement, performance optimization, release support, and ongoing maintenance across the full BI development lifecycle.
Provide training, documentation, and ongoing support to report consumers and business users.
Identify opportunities for business process optimization, reporting consolidation, self-service enablement, and data quality improvement.
Support continuous improvement of dashboard standards, naming conventions, documentation expectations, accessibility practices, and BI development best practices.
Collaborate with data engineers to design, refine, test, and monitor data pipelines and curated datasets that support enterprise analytics.
Develop and maintain data transformations, reusable queries, data models, and analytics-ready structures across structured, semi-structured, and unstructured sources.
Validate data completeness, accuracy, timeliness, and consistency across source systems, pipelines, semantic models, and reports; document and help resolve data quality issues.
Support ETL/ELT workflows, data integration requirements, source-to-target mappings, and troubleshooting of data movement across the Azure data ecosystem.
Contribute to scalable, maintainable analytics solutions through sound development practices, technical documentation, version control, deployment discipline, and monitoring.
Coordinate and perform testing to confirm that documented requirements and quality expectations are met.
Develop test plans, test cases, expected results, reconciliation approaches, and supporting evidence for data and BI solutions.
Manage and facilitate User Acceptance Testing (UAT), track issues through resolution, validate corrections, and obtain end-user approval.
Support project delivery lifecycle activities, including estimation, design review, backlog refinement, documentation, release readiness, and training.
Communicate status, risks, dependencies, decisions, and data issues to project stakeholders and delivery partners.
Balance multiple concurrent priorities while meeting agreed-upon delivery commitments and maintaining solution quality.
Create and maintain business requirements documents, technical requirements documents, data dictionaries, process flows, source-to-target mappings, test documentation, release notes, and user guides.
Ensure solutions align with confidentiality, privacy, security, accessibility, and data governance expectations.
Provide recommendations and actionable insights based on analytical findings and assist in the development of data-informed strategies and initiatives.
Stay current on developments in business intelligence, data engineering, analytics, artificial intelligence, and responsible data practices; recommend practical improvements when appropriate.