Analytics Engineer II - Finance

at  Kueski

POSITION REQUIREMENTS

• A degree in fields such as Engineering, Actuarial Sciences, Physics, Mathematics, or similar
• +5 years of experience as a Data Analyst / Business Analyst / Data Engineer or a similar discipline working closely with Finance teams
• Experience in working with P&L, Financial statements, Vintage / Cohorts analytics and other Finance methodologies to makes sense of financial data
• Strong experience in the Analysis and Design of Data Models,
• Strong understanding of domain concepts like Keys, Dimensions, Star schemas, snowflake schemas, OLAP, DWH, ODS, EDW, DataLake
• Experience with data analysis tools like Databricks, Astronomer, Airflow, DataCatalog, etc.
• Solid experience with manipulating data using SQL
• Experience implementing and automating data pipelines
• Experience with data building and modeling tools (data build tool) and version control systems (git)
• Experience with ETL/ELT design, development, optimization & testing
• Strong experience developing dashboards with BI tools like Tableau
• Experience working with statistical analyses and experiments: A/B tests, significance tests, etc.
• Strong skills to communicate clearly and effectively analyses and reports both to technical and non-technical audiences
• A clear focus on business objectives and results
• Provide technical guidance and mentorship to junior analytics engineers and team members.
• Fluency in English

PURPOSE

As an Analytics Engineer with strong capabilities to implement the best technologies for data processing and at the same time with a strong analytical drive to generate the best business outcomes.

KEY RESPONSIBILITIES

• Improve the way our company consumes data: develop and deploy trusted and well-governed datasets with a high impact on the company
• Extract data from primary or secondary data sources and maintain databases/data systems
• Work with large amounts of data (in an efficient and optimized fashion)
• Interpret data and analyze results using statistical techniques. Provide ongoing reports related to such business and data analyses
• Performing analyses to assess the quality of our data
• Develop and automate production-level data pipelines
• Leverage data visualization tools to represent the data in a business-friendly manner
• Locate and define new process improvement opportunities
• Automate and optimize recurrent and manual processes
• Full support to Finance and Accounting team
• Full understanding of the F&A processes
• To develop new data models with dbt, only for Finance & Accounting purposes
• Creation of dashboards to monitor inconsistencies
• Automation of the reports ready to be use for the Accounting & Finance team