Data Analyst

Our client is a leading professional services firm specializing in data-first strategies for today's dynamic digital landscape. Their innovative solutions, rooted in deep partnerships with major cloud providers and modern data platforms such as Databricks and Microsoft, streamline data delivery, unlock innovation, and build high-performing teams. They empowers organizations to create stronger connections with external partners and customers by emphasizing speed to market, quality, and automation, ensuring success in a data-driven world.

Job Summary:

We are seeking a detail-oriented and skilled Data Analyst to join our team. The ideal candidate will be adept at analyzing and interpreting data, using various reporting tools to answer business questions and provide valuable insights to support decision-making. This role requires excellent communication and collaboration skills, as the Data Analyst will work closely with business stakeholders to understand their needs and deliver actionable results.

Key Responsibilities:
1. Capture and Define Data Requirements: Collaborate with business stakeholders to capture data requirements, ensuring an understanding of their needs and key questions to be answered through data analysis. Document these requirements using Excel or other tools for efficient communication.
2. Defining Objectives and Key Results (OKRs): Collaborate with business stakeholders to capture OKRs related to the data platform. Work with the business to develop data use cases that align with and achieve these OKRs, ensuring data insights drive the organization's strategic objectives.
3. Data Categories and Entity Prioritization: Work with business leaders and project managers to prioritize data categories, entities, and domains for development, ensuring alignment with business needs and timelines.
4. Data Modeling and Architecture Design: Define and create medallion architectures (bronze, silver, and gold layers) using modern data modeling techniques such as Data Vault, Third Normal Form (3NF), Data Mesh for the Silver layer, and Kimball Star Schema, Snowflake Schema, or Denormalized Models for the Gold layer.
5. Entity Relationship Diagrams (ERD): Use Visio or similar tools to compose Entity Relationship Diagrams (ERD) that document the relationships and dependencies of the data model.
6. Report Prototyping and Requirements Documentation: Document report requirements, including source systems and existing "as-is" reports, and identify necessary "to-be" reports. Develop prototypes using modern reporting tools like Power BI or Tableau to ensure alignment with business expectations.
7. Documenting Data Governance Requirements: Collaborate with business leaders to document data governance requirements, ensuring compliance with industry standards and prioritizing data security, quality, and accessibility.
8. Documenting Future (To-Be) Systems: Document the "to-be" systems that will be required to support future data and reporting needs, ensuring alignment with the evolving requirements of the business.
9. Data Use Case Development: Work closely with the business to develop and document data use cases that align with OKRs, driving data-driven initiatives that support broader business goals.
10. Data Requirements for LLM and AI Models: Define and document specific data requirements for large language models (LLM) and AI models, ensuring that data inputs are properly prepared and aligned with AI-driven solutions.
11. Source System Documentation: Create comprehensive documentation of source systems, detailing their current data flow and integration points, and identify areas for improvement or new system integration.
12. Data Cataloging and Report Inventory: Develop and maintain a catalog of reports, both "as-is" and "to-be," to ensure transparency in data availability and streamline future reporting needs.
13. Investigating Data Quality: Investigate data quality issues and make recommendations for improvement. Collaborate with engineering and IT teams to resolve these issues, ensuring long-term data accuracy and reliability.
14. Analysis and Visualization: Utilize tools like SQL, Power BI, and Tableau to analyze and interpret data, providing stakeholders with insights through clear, visually appealing dashboards and reports.
15. Performance Review of Data Models: Regularly review the current data model, assessing any limitations or performance bottlenecks, and recommend optimizations to improve scalability, querying efficiency, or data quality.
16. Continuous Learning and Industry Best Practices: Stay current with industry trends, modern data architecture approaches, and best practices in data analysis and reporting, applying this knowledge to improve processes and tools within the organization.
17. Ad Hoc Data Analysis Support: Provide support for ad hoc data analysis requests and contribute to ongoing projects, ensuring timely and accurate delivery of insights.

Qualifications:
1. Minimum of 4 years of experience in a data analyst role or a similar position.
2. Solid understanding of statistical techniques and concepts, including data modeling, trend analysis, and hypothesis testing.
3. Excellent communication, both written and verbal.

Nice to Have:
1. Data visualization tools (e.g. Power BI).
2. Experience managing in a SCRUM environment
3. Knowledge of various machine learning techniques and their applications in solving business challenges, including supervised and unsupervised learning methods. although proficiency in coding is not required