Job Description

Roles: Technical Business Data Analyst

Location: NYC, NY or Dallas , TX

Duration:- 12+ Months

Education:

• Bachelor's degree in Computer Science, Information Technology, Business Administration, or a related field.
• Advanced certifications in data analytics and business analysis are a plus.

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.

Thanks

Chandra Shekhar Kumar

[email protected]

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