Senior Data Analyst

Indium Software is looking for Senior Data Analyst to join our dynamic team and embark on a rewarding career journey.

The Senior Data Analyst is responsible for analyzing large datasets, interpreting data, and providing actionable insights to support business decision-making. They will work closely with cross-functional teams to identify data needs, design data collection methods, perform data analysis, and communicate findings to stakeholders. The Senior Data Analyst plays a crucial role in driving data-driven strategies and improving overall business performance. Key Responsibilities : Collect, clean, and transform large datasets from various sources to ensure data accuracy and integrity. Analyze complex datasets using statistical techniques and data mining methods to identify trends, patterns, and insights. Interpret and communicate findings to both technical and non-technical stakeholders through reports, visualizations, and presentations. Collaborate with cross-functional teams to understand business requirements and develop data analysis plans to address specific objectives. Develop and maintain data models, dashboards, and reports to provide ongoing performance metrics and KPIs. Identify data quality issues, perform data validation, and implement data cleaning and data normalization processes. Utilize data visualization tools (e. g. , Tableau, Power BI) to create interactive dashboards and visual representations of data. Stay updated with industry trends, emerging technologies, and best practices in data analysis and data management. Identify opportunities to optimize data collection, analysis, and reporting processes, and implement efficient and automated solutions. Mentor and provide guidance to junior data analysts, fostering their professional growth and development. Qualifications and Skills : Bachelor's degree in statistics, mathematics, computer science, or a related field. A master's degree is a plus. X years of experience as a Data Analyst, preferably in a senior or lead role. Proficiency in statistical analysis tools such as R, Python, or SAS. Strong knowledge of data analysis techniques, including regression analysis, hypothesis testing, and data mining. Experience with data visualization tools, such as Tableau, Power BI, or QlikView. Solid understanding of SQL for data manipulation and extraction. Familiarity with data warehousing concepts and database systems (e. g. , SQL Server, Oracle). Excellent analytical and problem-solving skills, with attention to detail. Strong communication and presentation skills, with the ability to effectively communicate complex data insights to non-technical stakeholders. Ability to work independently and collaboratively in a fast-paced environment, handling multiple projects and priorities.