Sr Data Analyst

JOB SUMMARY

The primary purpose of this role is to analyze large-scale data sets to discover trends, identify performance and quality metrics, and uncover optimization opportunities as well as design and develop enterprise-wide data analysis and reporting solutions and fulfil ad hoc reporting requests.

ESSENTIAL FUNCTIONS/MAJOR JOB RESPONSIBILITIES

• Analyze enterprise data warehouse and business intelligence systems to identify and document complex data mappings (ETL), relations, business rules, as well as functional and non-functional requirements.
• Gather reporting and analysis requirements and translate into data models.
• Provide analytical support to various departments within the organization including formulating metrics and quality targets.
• Analyze and resolve data quality problems through appropriate process design strategies, including error detection and proposing error correction procedures, using automation wherever possible.
• Recommend methods to collect, analyze and manage data to improve data quality and effectiveness of data systems.
• Act as primary point of contact working with stakeholders to understand systems, processes and business needs.
• Work closely with Legal, Compliance and Security/Privacy to ensure the highest level of Healthcare data protection and quality.
• Support the Development Team during the software build process (consultancy, requirements definition, design, documentation, review, etc.)
• Support the sales / pre-sales process by ensuring data meets the client needs and analyzing development requirements from potential new sales prospects.
• Define and develop dashboards/reports and ad hoc queries as requested in a simple and understandable format.

JOB REQUIREMENTS

Education

• Bachelor's Degree in a relevant Program of study, or equivalent preferred

Work Experience 

• 7+ years relevant work experience; 2-3 years at the Expert level or equivalent experience preferred

Knowledge, Skills and Abilities

Required Skills

• Required Technologies: SQL, Excel, PowerBI, MicroStrategy, SSIS, SSRS, XML, PowerShell, Google Charts, JavaScript 
• Data Warehousing and Business Intelligence Systems: Profound knowledge of data storage, retrieval, and analysis systems.
• ETL Processes: In-depth understanding of Extract, Transform, Load (ETL) processes for integrating and processing data.
• Statistical and Analytical Methods: Strong foundation in statistical methods to apply in data analysis for uncovering trends and insights.
• Data Analysis and Visualization: Proficiency in analyzing large datasets and presenting data in visual formats using tools like Tableau, Power BI, or similar.
• Programming and Scripting: Expertise in programming languages such as SQL, Python, or R for data manipulation and analysis.
• Critical Thinking and Problem-Solving: Excellent analytical skills for solving complex problems and providing practical solutions.
• Communication Skills: Superior verbal and written communication skills, able to clearly explain complex data-related concepts to non-technical stakeholders.
• Translating Requirements into Data Models: Ability to convert business requirements into functional data models and analytics reports.
• Ensuring Data Quality and Accuracy: Capability to implement and maintain quality control processes ensuring data accuracy and validity.

Preferred Skills

• Database Management Systems (DBMS): Familiarity with both relational and non-relational database systems like SQL Server, Oracle, or MongoDB.
• Data Protection and Compliance: Knowledge of legal and regulatory requirements in data handling, especially relevant in healthcare.
• Project Management: Skills in managing data analysis projects from start to finish, ensuring timely delivery.
• Multitasking: Ability to handle multiple projects simultaneously without compromising on quality or deadlines.
• Collaboration: Strong interpersonal skills to work effectively with diverse teams including development, sales, and legal.
• Adaptability to New Technologies: Quick learner with the capability to adapt to new data analysis tools and technologies as they emerge.