Report and dashboard developers, acting as a subject-matter expert (SME) on the designed data assets. Gather, understand, and document functional and non-……
Participate in weekly professional development sessions led by the Sovos Leadership team and HR team. Gaining experience using tools-of-the-trade; Jira, Zephyr,……
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Descripción del empleo:
As a Data Analytics Intern, you will be expected to perform the following activities in alignment with your experience level:
Gather, understand, and document functional and non-functional requirements from stakeholders to support analytical solutions
Write or collaborate on the creation of business requirements documents (BRDs) detailing solution characteristics, business expectations, and anticipated value.
Create diagrams that illustrate high-level data domains and key concepts involved in solution design.
Document required measures and KPIs to support business objectives, including clear definitions and calculation logic.
Explore and understand data assets to be leveraged as sources for analytical solutions
Build and maintain detailed inventories of data assets required to deliver analytical solutions.
Profile data assets to understand their granularity, domain context, business meaning, definitions, data quality characteristics, and interrelationships.
Design solution architecture to consume, transform, and deliver data for analytical solutions – Data Marts / Data Lake
Design scalable and reusable logical data models for analytical repositories using relational and dimensional modeling techniques, integrating and conforming data from multiple sources.
Document entities, attributes, and relationships using logical data model diagrams.
Define detailed data movement requirements to populate modeled entities, including column mappings, joins, load strategies, and load dependencies.
Perform internal testing and system integration testing (SIT) on developed data assets.
Collaborate with cross-functional teams to deliver end-to-end analytical solutions, including:
Data Governance teams for integration of master and reference data
Data Services / Engineering teams for ETL development
Business stakeholders for user acceptance testing (UAT)
Data Quality teams to support periodic data quality assessments
Report and dashboard developers, acting as a subject-matter expert (SME) on the designed data assets
Production Support teams for troubleshooting and issue resolution
Implement data assets for serving data – Semantic Layer
Design and implement semantic-layer data assets to support reports, dashboards, self-service analytics, and other downstream consumption scenarios.
Create and maintain data dictionaries and documentation to enable and empower data consumers.
Demonstrate a solution‑owner mindset
Collaborate in backlog refinement, maintenance, and prioritization activities.
Actively participate in agile ceremonies, contributing to discussions on current work, next steps, dependencies, and risks.
Provide work breakdowns, effort estimates, and expected delivery timelines for assigned tasks.
Build and support diverse, high‑performing teams
Work effectively with a high degree of independence in a remote or hybrid environment.
Continuously expand professional value by learning emerging technologies and concepts and applying them to improve existing products, processes, and outcomes.
Required Qualifications – Experience, Skills, and Knowledge
Completion of at least the 2nd year and current enrollment in the 3rd or 4th year of an undergraduate program in Mathematics, Computer Science, Statistics, Business Management, Information Systems, or a related field
English proficiency at an advanced level (C1 or equivalent)
SQL: basic data querying (DQL) skills
Power BI: basic experience with report development or semantic model creation
Preferred Qualifications
Familiarity with Data Warehouses, Data Lakes, or Data Lakehouse architectures
Knowledge of relational and dimensional data modeling techniques
Exposure to agile methodologies such as SCRUM and tools like Azure DevOps
Awareness of cloud computing platforms such as Azure, Google Cloud, or Amazon Web Services, including Databricks
Interest or foundational knowledge in AI concepts, including prompting techniques or agent development
Exposure to Microsoft Fabric
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