Data Engineer - Data QA

Department:

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

• 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

• As a Data Engineer - Data QA & Workflow Integration Testing, you’ll sit at the intersection of creative assets and business outcomes, turning content taxonomy, DAM metadata, and workflow velocity into actionable marketing data and quality insights.

• You’ll work across enterprise-scale data ecosystems and MarTech schemas, using streaming asset event data and automated quality frameworks to improve Content Supply Chain measurement and reliability.

• The role has direct impact on how marketing leadership measures Content Supply Chain efficiency, ROI, asset utilization, and content-driven outcomes across enterprise touchpoints.

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

• Build data pipelines that validate digital-asset metadata schemas, content taxonomy, asset performance, and workflow data.

• Use SQL and Python to develop automated validation scripts for metadata tagging and XDM/schema mappings across DAM, AEM, and enterprise data warehouses.

• Build and test ETL pipelines that aggregate asset-utilization, digital-rights-management, and campaign-enrichment data from batch and streaming sources.

• Implement data-quality frameworks that detect metadata drift, missing campaign tags, schema errors, and duplicate asset records.

• Audit asset-tracking models so content velocity, localization variants, and asset-attribution metrics reconcile across Workfront, publishing platforms, and reporting dashboards.

• Support data-quality, governance, and monitoring practices across Content Supply Chain workflows.

• Partner with MarTech, DAM, analytics, and platform teams to resolve data and workflow integration issues.

• Translate data quality findings into actionable improvements for downstream analytics and marketing reporting.

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 and Python skills for data validation, scripting, ETL, and schema quality checks.

• Strong understanding of digital-asset metadata schemas, content taxonomy, DAM data, and workflow data.

• Strong data-quality framework design capability, including drift, completeness, duplicate detection, and schema validation.

• Experience with batch and streaming data pipelines and enterprise data warehouses.

• Ability to audit content and asset-tracking models and reconcile operational metrics.

• Strong analytical and problem-solving skills within MarTech and Content Supply Chain environments.

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

• Preferred:

  • Familiarity with Databricks.

  • Working knowledge of Customer Data Platforms (CDPs).

  • AWS monitoring experience.

  • CI/CD security and compliance exposure.

  • Python/SQL scripting depth.

  • AEM exposure is a nice to have; MarTech domain knowledge is required.

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.

Why This Role

• Turn Content Supply Chain data into reliable marketing and business insights.

• Improve asset utilization, content-quality measurement, and data integrity across enterprise touchpoints.

• Build data-quality foundations that help leadership measure efficiency, ROI, and content-driven outcomes.

Southwest Airlines is an Equal Opportunity Employer.

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