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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Solutions Engineer - **Company:** Paychex Inc. - **Location:** Rochester, NY, United States - **Experience:** Starter - **Salary:** $98,416.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Mining, Data Systems, DevOps, Github, Python (Programming Language), Linux System Administration, Machine Learning, Performance Tuning, Cloud Services, Tensorflow, Azure Machine Learning, Software Engineering, Systems Integration, Google Cloud, Azure Data Factory, System Availability, Snowflake, Multi-Cloud, HybridCloud, Information Technology, Machine Learning Operations, Api Design, Terraform, Data Pipelines, Atlassian Bamboo, Docker, Databricks - **Published:** July 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3044d3acb77883ad ## About the Role * Bachelor's Degree in Computer Science, Data Science, Engineering, or related field - Required * 7 years of experience in data engineering, software engineering, systems integration, or a related field, with demonstrated expertise in designing, building, and deploying scalable data solutions. * 4 years of experience in cloud technologies (Azure, AWS, Google Cloud) and large-scale data processing. * 1 year of experience in machine learning model deployment, AI/ML solutions, and data pipeline architecture. * Less than 1 year of experience in Familiarity with AI/ML frameworks, DevOps practices, and MLOps processes for integrating AI solutions. * Snowflake SnowPro - Preferred ## Description Overview: The Data Solutions Engineer will play a key role in integrating, architecting, and optimizing data systems to support data monetization, analytics, machine learning, artificial intelligence, and large-scale data operations. The role will involve collaborating with cross-functional teams to develop and deploy end-to-end solutions that improve data availability, scalability, security, and overall performance, ensuring alignment with both business goals and technical best practices. Responsibilities: Ownership Mindset & Cross-Functional Collaboration - Thrives in a fast-paced engineering culture, quickly learns new technologies, and collaborates effectively across engineering, security, and platform teams to deliver secure, compliant, production-ready solutions while supporting monitoring, incident response, and ongoing platform operations. * Data Platform & Databricks Administration - Strong data engineering background with experience designing, building, and supporting data pipelines and cloud data platforms. Hands-on experience administering Databricks environments, including workspace configuration, cluster management, access controls, governance, and performance optimization. * Azure Cloud & DevOps Engineering - Proven experience designing, building, and operating cloud-native solutions on Azure, including App Services, Container Apps, CI/CD pipelines, Terraform, container deployment, Linux administration, and overall platform engineering. * Advanced Python Application Development - Hands-on experience developing enterprise-grade applications using Python, including containerized solutions with Docker, API development, automation frameworks, and modern deployment practices with AI Exposure * Work with architects, operations teams, and data scientists to define data requirements and translate them into actionable data strategies. Design, build and optimize data systems for performance, scalability, ease of use and reliability, leveraging tools for observability and troubleshooting; includes exploration of multiple solution options for any given integration objective and analysis of associated advantages and disadvantages. Enhance system integration for data workflows, ensuring performance metrics are met and all integrations remain stable and secure. Collaborate on the integration of AI/ML platforms, ensuring seamless multi-cloud and hybrid cloud operations * Build and optimize data pipelines to support data extraction, transformation, and loading (ETL) processes using technologies such as Databricks, Snowflake, and Azure Data Factory. Develop automation frameworks and CI/CD pipelines using tools like Terraform, GitHub Actions, and Azure Pipelines for efficient and reliable data deployment. Ensure data pipelines comply with security, privacy, and compliance standards. * Work closely with internal teams, including data engineers, data scientists, analytics engineers and business stakeholders, to understand platform solution needs. Mentor junior engineers, providing guidance on best practices and technologies. Evangelize integration practices and knowledge within the organization to improve collaboration across teams. * Stay abreast of the latest trends in cloud computing, machine learning, AI, and data engineering. 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