Senior Data Analyst

Overview

Job Summary

Drive data-driven decision-making across the organization by designing, developing, and deploying robust analytical solutions. You will leverage your expertise in SQL, Python, and Power BI to transform complex data into actionable insights, build scalable data models, and create compelling visualizations that empower business stakeholders.

Under the general direction of the Technical Leader and senior engineering personel, with moderate latitude for self-direction, work closely with data engineers, BI analysts, product owners, and business stakeholders to deliver fast, efficient, and scalable analytical products and reporting capabilities for Werfen employees.

Responsibilities

Key Accountabilities

• Work with the team and business stakeholders to identify requirements, define models, and kickstart new analytical initiatives
• Develop and maintain complex, scalable data models within our data warehouse/lake environment using SQL. Utilize Python for data manipulation, automation, statistical analysis, and potentially building data transformation scripts to prepare data for analysis
• Develop and maintain a deep understanding of the company's data assets within the data lake/warehouse, serving as a subject matter expert for data availability, lineage, and quality
• Select and apply appropriate analytical methodologies and data modeling techniques (e.g., dimensional modeling, statistical analysis, segmentation) to address specific business questions
• Leverage creative solutions, analytics, and visualization/reporting techniques to enhance knowledge and drive actionable insights that deliver ROI
• Mentor junior analysts and share best practices in data analysis, modeling, and visualization techniques
• Champions, promotes, and practices data modeling best practices, code quality, and documentation
• Works with minimal oversight of the technical leader

Networking/Key relationships

To be determined based on department and project needs, to include interactions such as:

• Product Owner(s) and Technical Leader for Data Analytics

• Coordination with the Barcelona Data Group, Werfen IT
• Engineers and Scientists

Qualifications

Minimum Knowledge & Experience required for the position:

• Bachelor's degree in a quantitative field such as Computer Science, Engineering, Statistics, Mathematics, Economics, or a related STEM discipline. Master's degree is a plus
• 5 years of relevant work experience, within the healthcare industry preferred
• Proficiency in advanced SQL including window functions, CTEs, complex joins, and query optimization.
• Hands-on experience developing, testing, and deploying data transformation pipelines using dbt (data build tool).
• Strong proficiency in Python for data analysis, manipulation, and automation (experience with libraries like Pandas, NumPy, Scikit-learn is highly desirable).
• Expertise in data visualization and building interactive dashboards using Power BI (experience with DAX is essential). Familiarity with other tools like Qlik or Tableau is a plus.
• AWS, Microsoft Azure cloud computing, familiarity with DataOps principles and Agile methodologies, and Git software versioning, preferred

Skills & Capabilities:

• Team player, self-motivated, perseverance
• Strong oral and written skills
• Experience working with cloud-based data warehouses (e.g., Snowflake, Redshift, BigQuery, Azure Synapse) is highly desirable.
• Solid understanding of data warehousing principles and dimensional modeling concepts.
• Analyze business requirements in dialogue with internal stakeholders
• Strong analytical and problem-solving skills, with a keen attention to detail
• Proficiency in data analysis tools and programming languages (e.g., SQL, Python, R)
• Skilled in extracting insights from complex datasets
• Exhibits excellent time management and prioritization abilities
• Relevant certifications in data analysis, business intelligence, or data science (e.g., Google Data Analytics, Microsoft Certified: Data Analyst Associate) are a plus

Travel requirements: Less than 10% of the time.