Data Engineer

Ascendum Solutions; Cincinnati (OH); Hybrid; USD 100,000 - 130,000; Full time

Job summary

A technology solutions company in Cincinnati seeks a Data Engineer to implement modern data solutions in Azure. This role requires strong hands-on skills in Databricks, Spark, and Python. The ideal candidate will design and develop data products, utilizing cloud services and CI/CD practices. Responsibilities include building data pipelines, optimizing performance, and collaborating with engineering teams to enhance processes and security.

Qualifications

• 5+ years of experience as a Data Engineer.
• Hands-on skills in Databricks, Spark, and Python.
• Strong SQL and database background.

Responsibilities

• Analyze and develop enterprise data solutions using Azure and Databricks.
• Optimize Spark/PySpark data pipelines for performance.
• Implement CI/CD workflows using GitHub and GitHub Actions.

Skills

Experience with Azure Databricks

Python programming

SQL proficiency

Databricks SQL experience

Terraform for cloud infrastructure

GitHub and CI/CD practices

Strong understanding of distributed computing

Tools

Databricks

Azure Functions

GitHub Actions

Job description

• Candidates should be eligible to work for any employer in the United States without needing Visa sponsorship now or in the future
• Candidates must be currently located in the greater Cincinnati area
• Must be willing to interview in-person and work hybrid on-site

Description

Seeking a Data Engineer experienced in implementing modern data solutions in Azure, with strong hands-on skills in Databricks, Spark, Python, and cloud-based DataOps practices. The Data Engineer will analyze, design, and develop data products, pipelines, and information architecture deliverables, focusing on data as an enterprise asset. This role also supports cloud infrastructure automation and CI/CD using Terraform, GitHub, and GitHub Actions to deliver scalable, reliable, and secure data solutions.

Requirements

• 5+ years of experience as a Data Engineer
• Hands-on experience with Azure Databricks, Spark, and Python
• Experience with Delta Live Tables (DLT) or Databricks SQL
• Strong SQL and database background
• Experience with Azure Functions, messaging services, or orchestration tools
• Familiarity with data governance, lineage, or cataloging tools (e.g., Purview, Unity Catalog)
• Experience monitoring and optimizing Databricks clusters or workflows
• Experience working with Azure cloud data services and understanding how they integrate with Databricks and enterprise data platforms
• Experience with Terraform for cloud infrastructure provisioning
• Experience with GitHub and GitHub Actions for version control and CI/CD automation
• Strong understanding of distributed computing concepts (partitions, joins, shuffles, cluster behavior)
• Familiarity with SDLC and modern engineering practices
• Ability to balance multiple priorities, work independently, and stay organized

Responsibilities

• Analyze, design, and develop enterprise data solutions with a focus on Azure, Databricks, Spark, Python, and SQL
• Develop, optimize, and maintain Spark/PySpark data pipelines, including managing performance issues such as data skew, partitioning, caching, and shuffle optimization
• Build and support Delta Lake tables and data models for analytical and operational use cases
• Apply reusable design patterns, data standards, and architecture guidelines across the enterprise, including collaboration with sister Company when needed
• Use Terraform to provision and manage cloud and Databricks resources, supporting Infrastructure as Code (IaC) practices
• Implement and maintain CI/CD workflows using GitHub and GitHub Actions for source control, testing, and pipeline deployment
• Manage Git-based workflows for Databricks notebooks, jobs, and data engineering artifacts
• Troubleshoot failures and improve reliability across Databricks jobs, clusters, and data pipelines
• Apply cloud computing skills to deploy fixes, upgrades, and enhancements in Azure environments
• Work closely with engineering teams to enhance tools, systems, development processes, and data security
• Participate in the development and communication of data strategy, standards, and roadmaps
• Draft architectural diagrams, interface specifications, and other design documents
• Promote the reuse of data assets and contribute to enterprise data catalog practices
• Deliver timely and effective support and communication to stakeholders and end users
• Mentor team members on data engineering principles, best practices, and emerging technologies