> Markdown version of [/jobs/ext/4704821-principal-software-engineer-data-platform](https://www.wearedevelopers.com/jobs/ext/4704821-principal-software-engineer-data-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Principal Software Engineer (Data Platform)

- **Company:** S Linx LLC
- **Location:** Dallas, TX, United States
- **Contract:** Contract
- **Skills:** Clean Code Principles, Airflow, Amazon Web Services, Application Frameworks, Command-Line Interface, Cloud Computing, Cloud Engineering, Configuration Management, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Software Debugging, DevOps, Distributed Computing Environment, Python (Programming Language), Object-Oriented Software Development, Query Optimization, Regression Testing, Software Engineering, Workflow Management Systems, Enterprise Data Management, Data Logging, Cloud Platform System, Apache Spark, AI Coding Agents, Fastapi, Solid Principles, Kubernetes, Apache Kafka, Software Version Control, Data Pipelines
- **Published:** November 13, 2026
- **Apply:** https://www.dice.com/job-detail/51cd19a4-6800-45cb-93ed-b49c2ee2270b

## About the Role

* Strong Python software development experience, including object-oriented programming, classes, modules, functions, parameters, exception handling, and reusable component design.
 * Proficiency with Python logging, configuration management, command-line parameters, debugging, and error-handling patterns.
 * Experience writing unit, integration, and regression tests for Python applications and transformation logic.
 * Knowledge of software design principles, maintainable coding practices, source control, code reviews, and Python packaging.
 * Strong SQL development and query optimization.
 * Data engineering and ETL/ELT pipeline development experience.
 * Experience with Apache Spark and distributed data processing.
 * Python experience for data transformation and automation.
 * Experience with workflow orchestration platforms such as Apache Airflow or similar.
 * Knowledge of data modeling and data warehousing concepts.
 * Experience with Kubernetes and containerized data workloads.
 * Experience with cloud-native platforms and AWS services.
 * Experience with CI/CD and DevOps practices.
 * Experience with observability, monitoring, and operational analytics.
 * Knowledge of data quality, lineage, and governance practices.
 * Demonstrated focus on automation and operational excellence.
 * Expert-level software engineering and architecture capability with demonstrated ability to establish Python, data-platform, cloud-native, and DevOps patterns and standards across teams.
 * Demonstrated enterprise data-platform engineering leadership, including complex architecture decisions, systemic problem solving, technical governance, and capability transfer.

## Description

* Define technical direction and engineering standards for production-quality Python applications, reusable components, packaging, configuration, logging, testing, debugging, and maintainability.
 * Provide architecture leadership for scalable ETL/ELT pipelines, Spark-based processing, workflow orchestration, and Kubernetes-native data workloads.
 * Well versed with streaming applications Kafka.
 * Able to build optimized pipelines to ingest data.
 * Establish reference patterns and reusable frameworks that improve developer productivity, platform consistency, self-service, and operational efficiency.
 * Drive architecture and engineering decisions that improve data pipeline reliability, performance, scalability, observability, and recoverability.
 * Lead resolution of systemic software, pipeline, infrastructure, performance, and data-quality issues spanning multiple technology layers and teams.
 * Establish cloud-native development, CI/CD, DevOps, source-control, code-review, and engineering-governance practices for the Software Engineering workstream.
 * Guide Software Engineers and Senior Software Engineers through complex design decisions and ensure solutions remain maintainable, supportable, and reusable.
 * Partner with data consumers, application teams, architecture, and platform engineering to ensure data-platform capabilities support enterprise use cases and reliable dataset delivery.
 * Establish responsible patterns for AI-assisted software development and support solutions, with appropriate technical validation and engineering controls.
 * Lead capability transfer and documentation so client teams can operate, extend, and support delivered solutions without vendor dependency.