SWA Business Intelligence Engineer, EU SWA Performance Team

We are looking for a talented and driven Business Intelligence Engineer to join the SWA Performance this role you will play a critical part in shaping how our team understands and improves operational performance by transforming complex data into actionable insights.

You will design build and maintain scalable BI solutions including dashboards reports and data pipelines that empower stakeholders to make data-driven decisions. You will work closely with operations finance and product teams to define metrics surface trends and drive continuous improvement across our performance programs.

Key Responsibilities
- Design and develop dashboards reports and self-service analytics tools to monitor SWA performance metrics
- Build and maintain robust data pipelines and data models to support reporting and analysis needs
- Partner with cross-functional stakeholders to gather requirements and translate business questions into analytical solutions
- Define document and standardize key performance indicators (KPIs) across the SWA performance program
- Identify trends anomalies and opportunities through deep-dive analysis and proactive data exploration
- Ensure data quality accuracy and consistency across all BI outputs
- Collaborate with engineering and data teams to improve data infrastructure and accessibility

Basic Qualifications
- Bachelors degree in Computer Science Engineering Statistics or a related field
- Experience in a Business Intelligence Data Engineering or Analytics role
- Proficiency in SQL and experience working with large-scale data warehouses
- Experience building dashboards and visualizations using tools such as Amazon QuickSight Tableau or similar
- Strong analytical and problem-solving skills with attention to detail
- Ability to communicate complex data findings clearly to both technical and non-technical audiences

Preferred Qualifications
- Experience working in a logistics transportation or operations environment
- Familiarity with Python or other scripting languages for data processing and automation
- Experience with ETL/ELT pipelines and data modeling best practices
- Demonstrated ability to work in a fast-paced ambiguous environment and manage multiple priorities
- Experience with Amazon Web Services (AWS) data services (e.g. Redshift S3 Glue)

Key job responsibilities
Key Responsibilities
- Design develop and maintain interactive dashboards and reports to monitor and communicate SWA performance metrics to stakeholders at all levels
- Build and optimize scalable data pipelines and data models that support reliable timely reporting and self-service analytics
- Define document and standardize key performance indicators (KPIs) aligned with SWA performance program goals
- Partner with operations finance and cross-functional teams to translate business questions into analytical solutions and actionable insights
- Conduct deep-dive analyses to identify performance trends anomalies and improvement opportunities across SWA programs
- Ensure data quality accuracy and consistency across all BI outputs proactively identifying and resolving data issues
- Collaborate with data engineering and product teams to improve data infrastructure accessibility and governance
- Support ad hoc analytical requests and present findings clearly to both technical and non-technical audiences
- Continuously evaluate and improve existing BI tools and processes to increase efficiency and impact




- Experience in analyzing and interpreting data with Redshift Oracle NoSQL etc.
- Experience with data visualization using Tableau Quicksight or similar tools
- Experience with data modeling warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Experience with SQL
- Experience in the data/BI space

- Experience with AWS solutions such as EC2 DynamoDB S3 and Redshift
- Experience in data mining ETL etc. and using databases in a business environment with large-scale complex datasets

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