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.
-
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
-
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
