Technical Business Data Analyst

Technical Business Data Analyst/Azure Data/Capital Markets/Hedge Fund

Hybrid/ Dallas, TX 3 days a week.

Experience Required:

1. How many years working with: Technical Data Business Analyst

2. How many years working with: Data Warehouse

3. How many years working with: Azure Databricks (highlighted expertise) Logic Apps - Ect

4. How many years working with: SQL and ETL

5. How many years working with: Capital Markets/Hedge Funds /BANKING

Technical Skills:

• Data Warehouse Expertise: In-depth knowledge of data warehouse core concepts, including data architecture, data integration, and data governance. Strong understanding of logical data modeling and relational database design.
• Query Writing: Proficiency in writing SQL queries for data extraction, analysis, and reporting.
• Azure Services: Experience working with Azure cloud services, including (preferred but not mandatory): Azure SQL Server, Azure Data Factory (ADF), Azure Databricks.
• Tools & Technologies: Familiarity with ETL tools like SSIS. Exposure to data visualization tools such as Power BI or Tableau.
• Industry Knowledge: Prior experience in the financial industry is a strong advantage, with knowledge of financial data structures and reporting requirements.

Soft Skills:

• Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
• Excellent communication and interpersonal skills to collaborate effectively with technical teams and business stakeholders.
• Ability to work independently and manage multiple priorities in a fast-paced environment.
• Attention to detail and a commitment to delivering high-quality work.

Responsibilities:

1. Requirements Gathering: Collaborate with business stakeholders to gather and document requirements for data engineering projects. Translate business needs into technical specifications for data pipelines, data models, and reporting solutions.
2. Data Analysis: Analyze large datasets to identify trends, patterns, and insights that support business decision-making. Write and optimize SQL queries to extract and manipulate data for analysis.
3. Data Modeling: Design and maintain logical data models to support data warehouse architecture and reporting needs.
4. Collaboration with Data Engineers: Work closely with senior and junior data engineers to ensure data pipelines and ETL processes align with business requirements. Provide input on database schema design and optimization.
5. Azure Services Utilization: Leverage Azure cloud services to support data integration, storage, and analytics workflows. Collaborate on projects involving Azure Data Factory, Azure SQL Server, and Databricks.
6. Documentation: Create and maintain comprehensive documentation for business requirements, data models, and technical workflows.
7. Financial Industry Insights: Apply knowledge of financial data structures and reporting standards to ensure compliance and accuracy in data solutions.
8. Technology Evaluation: Stay updated with emerging technologies and best practices in data analysis and business intelligence. Recommend new tools and technologies to improve efficiency and scalability.