Software Engineer - Data Platform Enablement (Cincinnati Metropolitan Area)

A little about this gig

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.

Responsibilities include:

• Designing and developing reusable services, libraries, and tools supporting common data movement and platform integration patterns.

• Translating platform-user feedback into repeatable solutions adopted across multiple engineering teams.

• Contributing to technical design conversations and shaping how new capabilities are implemented and supported.

• Developing services and workflows that move data between major cloud environments, including technologies such as BigQuery, Google Cloud Storage, feature stores, Databricks, and Azure-based data platforms.

• Building and enhancing Python and FastAPI services used to copy or distribute data between cloud products and testing environments.

• Building and maintaining production REST APIs using Python and FastAPI.

• Developing services that consume and produce messages across message broker topics.

• Creating reliable, testable integrations between internal systems, cloud services, and data platforms.

• Deploying and supporting services running in containerized environments such as Kubernetes.

• Contributing to feature-store migrations and upcoming legacy data migration initiatives, including translating requirements into epics and reusable platform patterns.

• Contributing to an automated AI agent that interacts with work-tracking systems to create and manage engineering work items.

• Using AI-enabled development tools to improve engineering productivity, code quality, testing, and documentation.

• Supporting approximately six Scrum teams focused on personalization, machine learning, search, and recommendations.

• Participating actively in Scrum ceremonies, code reviews, architecture discussions, and user acceptance testing.

The ideal candidate

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

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

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

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

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

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

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

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

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

• Experience working within an Agile or Scrum delivery model.

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

• Must be based in the United States, with a preference for candidates able to work primarily within Eastern or Central time-zone hours.

Preferred:

• Experience working across both GCP and Azure.

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

• Experience developing or deploying services in Kubernetes.

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

• Experience supporting data or platform migrations.

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

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

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

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

Tools and technologies used in this role:

Python, FastAPI, REST, Pydantic, PySpark, Spark, SQL, GCP, Azure, BigQuery, GCS, Databricks, feature stores, Kubernetes, containerized services, Kafka or similar message brokers, pytest, mocking, integration and regression testing, Git, GitHub, CI/CD, Jira, Confluence, Microsoft Teams.

Reasons to love it

• Opportunity to work at the platform level, building reusable tools and services adopted across multiple engineering teams rather than maintaining isolated pipelines.

• Exposure to a broad and modern technology landscape spanning Python, cloud platforms (GCP and Azure), Databricks, Kubernetes, and message-driven architectures.

• Hands-on involvement in cutting-edge AI-enabled automation, including contributing to an AI agent that supports engineering workflows.

• Collaborative, design-driven culture with active participation in architecture discussions, code reviews, and Agile ceremonies.

• Work alongside and support teams focused on high-impact domains such as personalization, machine learning, search, and recommendations.

• Role suited to engineers who enjoy variety, learning quickly, and solving cross-cutting platform challenges rather than narrow, single-tool specialization.

• Emphasis on collaboration and shared ownership of platform design and delivery rather than isolated individual-contributor work.