As a Senior Data Analytics Engineer at Stori you will leverage your analytics and engineering skills along with cutting edge big data technology to innovate, design, build, and maintain well-managed data products to solve complex business problems. This role will be responsible for leading the development of high quality, monitored and documented data products that will enable advanced analytics used for the decision making of several business analysts and business leaders.

• A bachelor's degree or foreign equivalent in Engineering, Science, Operations Research, Information Technology, Statistics, Mathematics, Economics, Finance, Analytics, or a related quantitative analytical field.
• Intermediate written and spoken communication skills in English
• Past experience in the financial sector is desirable but not required
• 2 or more years of experience in quantitative and qualitative analysis using SQL and Python.
• 2 or more years of working experience in data analytics
• 1 or more years of experience in DBT labs
• Experience with business intelligence tools (i.e. Quicksight, Looker, Redash )
• Collaborate with the Platform team to identify opportunities for leveraging data analytics to improve transactions reporting and analysis
• Design, develop, and maintain data pipelines, ETL processes, and data models to support financial data analysis and reporting requirements using DBT labs
• Develop and automate reports, dashboards, and visualizations to communicate key performance metrics and insights to stakeholders.
• Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
• Continuously monitor data quality and integrity, and implement measures to ensure accuracy and reliability of financial data.
• Conduct ad-hoc analyses and provide data-driven insights to support strategic decision-making and business planning initiatives.
• Participate in cross-functional projects and initiatives to enhance data integration, automation, and scalability across the organization.
• Collaborate with internal stakeholders to define data requirements and specifications for new financial products, services, and initiatives.
• Adhere to industry best practices for data governance by documenting data processes, systems, and methodologies to promote transparency, reproducibility, and knowledge sharing
• Conduct performance tuning and optimization of database queries and data processing jobs to improve efficiency and reduce latency in financial data reporting and analysis.
• Design and implement data quality checks and validation rules to ensure the accuracy, completeness, and consistency of financial data across multiple systems and platforms.