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# Software Engineer - Data Platform Enablement

- **Company:** Agility
- **Location:** Mundelein, IL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Microsoft Azure, BigQuery, Cloud Storage, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Systems, Distributed Computing Environment, Distributed Data Store, Distributed Systems, Python (Programming Language), Machine Learning, Message Broker, Scrum Methodology, Regression Testing, Cloud Services, Software Engineering, Feature Store, Data Processing, Google Cloud, Cloud Platform System, Apache Spark, Core Api, Agentic-AI, Git, Fastapi, Pyspark, Kubernetes, Apache Kafka, Restful APIs, Databricks
- **Published:** October 19, 2026
- **Apply:** https://www.thejobnetwork.com/job/a5e93f12-49f8-4b62-8125-7214d3f13a39/software-engineer-data-platform-enablement

## About the Role

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 * 3 to 5 years of professional software engineering, platform engineering, or data engineering experience.

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 * Strong Python development skills, including building reusable modules, services, and production-quality applications.

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 * Experience building and supporting production REST APIs, preferably using FastAPI.

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 * Experience with data processing technologies such as PySpark, Spark, or similar distributed computing frameworks.

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 * Solid understanding of data movement patterns, data integration, and distributed systems.

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 * Experience with at least one major cloud platform, preferably GCP or Azure.

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 * Ability to move fluidly between APIs, data systems, cloud services, message-based integrations, and platform tooling.

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 * Experience writing unit, integration, and regression tests for production software.

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 * Familiarity with Git-based development, CI/CD practices, and structured code review.

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 * Experience working within an Agile or Scrum delivery model.

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 * Strong communication and collaboration skills, including the ability to incorporate user feedback into technical solutions.

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 * Must be based in the United States, with a preference for candidates able to work primarily within Eastern or Central time-zone hours., * Experience working across both GCP and Azure.

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 * Familiarity with BigQuery, Google Cloud Storage, Databricks, or feature-store technologies.

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 * Experience developing or deploying services in Kubernetes.

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 * Experience with Kafka or another message broker, including consuming/producing messages across topics.

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 * Experience supporting data or platform migrations.

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 * Familiarity with platform enablement, internal developer platforms, or reusable engineering tooling.

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 * Experience with machine learning, personalization, recommendations, or search platforms.

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 * Familiarity with AI agents, AI-assisted development tools, or AI-enabled workflow automation.

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 * Experience translating user feedback into scalable, repeatable platform capabilities.

## Description

Our client, a leading retail organization with a large-scale technology platform, is seeking a versatile Software Engineer to join its Data Platform Enablement team. This team supports the engineering groups responsible for search, recommendations, personalization, and machine learning capabilities by building reusable tools, services, and data movement patterns that allow platform users to move data reliably across environments, cloud platforms, and downstream systems., Unlike a traditional data engineering role, this is not primarily about building and maintaining individual ETL pipelines. Instead, the focus is on creating repeatable platform capabilities - cross-cloud data movement services, platform APIs, automation tools, testing utilities, and AI-enabled workflows - that other engineering teams can adopt. The ideal candidate will be comfortable moving fluidly across Python, GCP, Azure, Databricks, Kubernetes, REST APIs, message brokers, and distributed data platforms, solving platform-level engineering problems rather than specializing narrowly in a single tool.