Location: Cincinnati, OH
Salary: $65.00 USD Hourly - $70.00 USD Hourly
Description: About the RoleWe are seeking an experienced
Data Engineer with strong hands-on expertise in building and operating modern data solutions on
Azure. You will design, develop, and optimize data pipelines and data platforms using
Databricks, Spark, Python, and cloud-native DataOps practices. The role also involves supporting infrastructure automation and CI/CD processes using
Terraform, GitHub, and GitHub Actions, ensuring the delivery of scalable, secure, and reliable enterprise data solutions.
Minimum Qualifications- 5+ years of experience as a Data Engineer
- Strong hands-on experience with Azure Databricks, Spark, and Python
- Experience with Delta Live Tables (DLT) or Databricks SQL
- Strong SQL skills and a solid background in relational and distributed databases
- Experience with Azure Functions, messaging systems, or orchestration tools
- Familiarity with data governance, lineage, and catalog solutions (e.g., Purview, Unity Catalog)
- Experience monitoring and optimizing Databricks clusters and workflows
- Understanding of Azure cloud data services and their integration with Databricks
- Proficiency with Terraform for infrastructure provisioning
- Experience with GitHub and GitHub Actions for version control and CI/CD pipeline automation
- Strong understanding of distributed computing concepts (joins, shuffles, partitions, cluster behavior)
- Familiarity with modern SDLC and engineering best practices
- Ability to work independently, manage multiple priorities, and stay organized
Preferred Qualifications- Experience with enterprise-scale data platform engineering
- Strong communication, documentation, and cross-team collaboration skills
- Ability to guide teams on data engineering best practices and emerging technologies
Key Responsibilities- Design and develop large-scale data solutions using Azure, Databricks, Spark, Python, and SQL
- Build, optimize, and maintain Spark/PySpark pipelines, addressing performance tuning, data skew, partitioning, caching, and shuffle optimization
- Create and manage Delta Lake tables and data models for analytical and operational workloads
- Apply reusable design patterns, data standards, and architecture guidelines across the organization
- Use Terraform to provision and manage cloud and Databricks resources following Infrastructure-as-Code (IaC) practices
- Implement and maintain CI/CD workflows using GitHub and GitHub Actions
- Manage Git-based workflows for notebooks, jobs, and data engineering artifacts
- Troubleshoot pipeline issues and improve stability across Databricks jobs, workloads, and clusters
- Deploy fixes, optimizations, and enhancements in Azure environments
- Collaborate with engineering and architecture teams to enhance tooling, processes, and data security
- Contribute to the development of data strategy, standards, and roadmaps
- Prepare architectural diagrams, interface specifications, and technical documentation
- Promote the reuse of data assets and support enterprise metadata and cataloging practices
- Provide effective communication and support to stakeholders and end users
- Mentor teammates on data engineering principles, frameworks, and best practices
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