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:
About Us
At Southwest Airlines, we believe that flying should feel like freedom. For over 50 years, we’ve been connecting people to what matters most with low fares, legendary hospitality, and a heart for service. Our culture is built on the principles of fun, friendliness, and family, making us one of the most admired workplaces in the world.
If you’re passionate about making a difference, love working in a fast-paced environment, and want to help millions of travelers reach their destinations with a smile, Southwest Airlines is the place for you. Here, your career can truly take flight.
Job Summary
- Southwest's Analytics & AI Pod partners across the business to turn data into decisions — building the dashboards, datasets, KPI frameworks, and AI-enabled analytical products that power operational and strategic decision-making. The team works at the intersection of Analytics, GenBI, self-service analytics, and modern data platforms, delivering trusted insights to stakeholders across Southwest.
- As Senior Data Engineer – Analytics & AI, you'll build and evolve the analytical datasets, semantic layers, and data pipelines that power Analytics & AI capabilities across the business. You'll partner closely with Business Data Analysts, AI Engineers, and Product Owners to design scalable data models, deliver GenBI-ready datasets, and modernize how analytical data is delivered from our Hyderabad office — bringing engineering rigor to how analytics products get built at Southwest.
- Work on complex problems, where analysis of situations or data requires an in-depth evaluation of multiple factors. Lead and/or provide expertise to functional project teams and participate in cross-functional initiatives. Provide direction and guidance to process improvements, including helping to establish/advise on policies. Work with a number of external vendors, helping to provide them with effective solutions and insights. Use independent judgment within broadly defined policies and practices, including determining the best method for accomplishing work.
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
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 the implications of 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.
- Preferred:
- Advanced AWS data services and modern data platforms (S3, Glue, Athena, Redshift, Snowflake, or comparable)
- Skilled in designing analytical data models and semantic layers optimized for BI and self-service consumption
- Python and advanced SQL for building analytical datasets and data pipelines
- Databricks or comparable lakehouse platform for analytical data engineering
- Data quality, validation, and reconciliation frameworks for analytical products
- Partnering with Business Data Analysts, AI Engineers, and Product Owners to translate analytical requirements into engineered datasets
- Data pipeline observability and reliability for analytical workloads
- Working knowledge of GenBI, conversational analytics, or AI-enabled analytics dataset requirements
Education
- Required: Bachelor's degree in Computer Science, Engineering, Information Systems or related field and/or equivalent formal training
Experience
- Required: Advanced level experience, seasoned and specialized 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. 5-7 years of relevant work-related experience
- Preferred:
- Building analytical datasets and data pipelines for enterprise BI, GenBI, or AI-enabled analytics consumers
- Delivering on semantic layer, KPI frameworks, or business glossary initiatives
- Databricks, Snowflake, or comparable modern analytics data platform experience
- Vector databases or embeddings pipelines supporting AI-enabled analytics
- Delivering analytical data solutions in Agile environments alongside Business Data Analysts and AI Engineers
- Working across enterprise business domains (Customer, Commercial, Marketing, Brand, Servicing)
- Data platforms serving Analytics, ML, and AI workloads within a large enterprise
- Working across globally distributed analytics and engineering teams
Other Qualifications
- Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
- Ability to work extended hours as needed
Southwest Airlines is an Equal Opportunity Employer.
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