Senior Data Analyst

2 weeks ago

Mountain View, United States DynPro Inc. Full time

As the Senior Data Analyst, you will be responsible for providing strategic direction, leading the development of data analytics frameworks, and effectively managing and analyzing complex data sets to identify trends, patterns, and opportunities that drive operational efficiency and business growth.

Collaborating closely with cross-functional teams, you will play a vital role in developing data-driven solutions, designing and implementing data models, and leveraging cutting-edge technologies to support decision-making processes and improve overall business performance.


Technical Qualifications:

  • Strong knowledge of data analysis tools and programming languages (e.g., Python, SQL, R) to extract, clean, manipulate, and analyze large datasets.
  • Proficiency in data visualization tools such as Power BI/Tableau to create meaningful and insightful reports and dashboards.
  • Experience with database systems (e.g., Databricks, Snowflake, BQ, Oracle, Microsoft SQL Server) and cloud data warehousing concepts.
  • Excellent problem-solving skills with a keen eye for detail and ability to see the bigger picture.
  • Experience with Automation and Understanding of AI/ML platforms and data requirements to facilitate development of AI/ML models
  • Strong communication and interpersonal skills, with the ability to effectively collaborate with and present findings to technical and non-technical stakeholders.
  • Demonstrated leadership skills, with the ability to mentor and guide a team of analysts.
  • Strong organizational skills and ability to manage multiple projects simultaneously.
  • Stay up-to-date with the latest advancements and best practices in data analysis, data management, and data visualization techniques.


Key Responsibilities:

  • Play a crucial, lead role in departmental/company data analytics efforts.
  • Collaborate closely with cross-functional teams, including Finance, Sales Operations, and Engineering, to identify business needs and establish data analysis requirements.
  • Design and implement data collection, integration, and retention strategies to ensure reliable and high-quality datasets.
  • Develop, maintain, and enhance data models, algorithms, and statistical models to extract insights and forecast key business metrics.
  • Analyze complex datasets using advanced statistical techniques and data visualization tools to identify trends, patterns, and correlations.
  • Present analytical findings and recommendations to key stakeholders, including senior management, in a clear and concise manner.
  • Drive the deployment of analytical solutions and tools, providing support and training to end-users for effective utilization.


Regards,

Gaganpreet Singh

Senior Talent Executive

www.dynpro.com

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