Data Engineer - AI & Analytics

Department:
Technology
Our Company Promise
We are committed to provide our Employees a stable work environment with equal opportunity for learning and personal growth. Creativity and innovation are encouraged for improving the effectiveness of Southwest Airlines. Above all, Employees will be provided the same concern, respect, and caring attitude within the organization that they are expected to share externally with every Southwest Customer.
Job Description:
Job Summary
Work with limited supervision to support data engineering and data analytics solutions. Provide design guidance and implementation for end-to-end analytic solutions. Coordinate with SWA focused groups to create unique data infrastructure, run tests on their designs to isolate errors and update systems to accommodate changes in company needs
Responsibilities
Assemble large, complex sets of data that meet non-functional and functional business requirements
Identify, design and implement internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
Build required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies
Build analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition
Work with stakeholders including data, design, product and executive teams and assisting them with data-related technical issues
Work with stakeholders including the Executive, Product, Data and Design teams to support their data infrastructure needs while assisting with data-related technical issues
Generate or adapt equipment and technology to serve user needs
May perform other job duties as directed by Employee's Leaders
Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
Lead scalable enterprise data engineering foundations supporting Agent Observability, Ops Enablement, and future autonomous operations.
Own telemetry ingestion, data processing pipelines, evaluation datasets, and reusable data engineering frameworks on AWS.
Serve as the technical partner across Product, AI, Operations, and Engineering teams while improving platform reliability, governance, and scalability.
Knowledge, Skills and Abilities
Knowledge of the practical application of engineering science and technology, including applying principles, techniques, procedures, and equipment to the design and production of various goods and services
Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models
Ability to use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems
Ability to understand new information for both current and future problem-solving and decision-making
Skilled in identifying complex problems and reviewing related information to develop and evaluate options and implement solutions
Ability to recognize when an issue has occurred or is likely to occur, without needing to diagnose or resolve the problem.
Ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events)
Ability to switch efficiently between multiple tasks or information sources, such as spoken instructions, system alerts, or data inputs.
Ability to organize data or actions in a defined sequence or structure based on specified rules or patterns (e.g., numerical, textual, visual, or mathematical sequences).
Ability to recognize defined patterns—such as shapes, words, or signals—even when they are embedded within distracting or complex information.
Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
Databricks and Apache Spark
Iceberg, Delta Lake, and Hudi
Kafka, Kinesis, and streaming architectures
AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana
Education
Required: Bachelor's degree in Computer Science, Engineering, Information Systems or related field and/or equivalent formal training
Experience
Required: Intermediate level experience, fully functioning broad knowledge in:
Cloud infrastructure, DataLake
ETL experience ensuring source to target data integrity
Various filetypes (Delimited Text, Fixed Width, XML, JSON, Parque).
ServiceBus, setting up ingress and egress within a subscription, or relevant AWS Cloud services administrative experience.
Unit Testing, Code Quality tools, CI/CD Technologies, Security and Container Technologies
Agile development experience and Agile ceremonies and practices
2-5 years of relevant work-related experience
Advanced SQL development, data modelling, performance tuning, and enterprise-scale analytical datasets.
Advanced Python development for reusable data frameworks, automation, testing, and production-grade engineering
Design and ownership of scalable ETL/ELT architectures, data lake, lakehouse, and enterprise integration patterns.
Advanced AWS data engineering experience across Glue, S3, Athena, Redshift, EMR, Kinesis, Step Functions, Airflow/MWAA, security, and reliability.
Data quality, governance, APIs, CI/CD pipelines, observability, automated testing, and production support leadership
Preferred Experience
Databricks and Apache Spark
Iceberg, Delta Lake, and Hudi
Kafka, Kinesis, and streaming architectures
AI/GenAI telemetry, vector databases, RAG analytics, QuickSight, or Grafana
Other Qualifications
Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
Ability to work extended hours as needed
Ability to work onsite approximately 3 days per week and as required by the business.
Southwest Airlines is an Equal Opportunity Employer.
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