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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - Databricks / Snowflake / Azure - **Company:** MS Companies - **Location:** Indianapolis, IN, United States - **Experience:** Expert - **Salary:** $130,000.0 - $155,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Application Frameworks, Microsoft Azure, Bash Shell, Cloud Computing, Cloud Database, Information Systems, Information Engineering, Data Governance, Data Integrity, Extract Transform Load (ETL), Data Visualization, Data Warehousing, Relational Databases, Linux, Monitoring of Systems, Python (Programming Language), Log Analysis, Operational Databases, Performance Tuning, Power BI, Cloud Services, Standard Sql, Azure Data Lake, Scala (Programming Language), SQL Databases, Tableau (Software), Management of Software Versions, Workflow Management Systems, Enterprise Data Management, Data Storage Technologies, Azure Data Factory, Snowflake, Data Strategy, Pandas, Data Lakes, Pyspark, Information Technology, Azure Synapse Analytics, Data Pipelines, Docker, Databricks - **Published:** November 5, 2026 - **Apply:** https://www.thejobnetwork.com/job/6d7511d6-7022-4328-9f7b-a42bff3ed2a2/senior-data-engineer-databricks-snowflake-azure ## About the Role * Minimum of 5 years of professional Data Engineering, Cloud Data Engineering, or closely related experience * Demonstrated, hands-on experience developing and supporting Databricks in a production environment * Strong professional experience with Snowflake cloud data platforms * Advanced development experience with Python, PySpark, and SQL * Proven experience designing, developing, and supporting scalable ETL/ELT pipelines * Strong understanding of cloud data architecture, preferably within Microsoft Azure * Experience with enterprise data warehousing, relational databases, schema design, data modeling, and performance optimization * Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical discipline * Must be authorized to work in the United States without current or future employer sponsorship Preferred Skills & Qualifications * Experience with Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, and Azure DevOps * Hands-on experience with Delta Lake, including data reliability, performance, optimization, and versioning * Experience implementing or supporting Databricks Unity Catalog and enterprise data-governance practices * Experience developing automated CI/CD pipelines for data engineering workflows * Experience with workflow orchestration through Databricks Jobs, Azure Data Factory pipelines, or similar tools * Experience monitoring and optimizing Databricks clusters for performance, reliability, scalability, and cost efficiency * Familiarity with Azure Log Analytics, Databricks monitoring tools, or custom platform-performance dashboards * Exposure to Power BI, Tableau, or other enterprise reporting and data-visualization tools * Working knowledge of Scala, Pandas, Linux, Bash, Docker, or related engineering technologies * Experience supporting financial, operational, ERP, manufacturing, supply-chain, construction, or enterprise business data * Ability to communicate technical information clearly to both technical and non-technical stakeholders, including business and executive leadership, The role requires the ability to manage multiple priorities, analyze complex technical issues, collaborate across functional teams, and present technical information to audiences with varying levels of technical expertise. ## Description This role is designed for an experienced, hands-on data engineer who has built and supported production cloud data environments and is prepared to serve as a technical subject-matter expert across Databricks, Snowflake, Python, SQL, and the Microsoft Azure ecosystem. The Senior Data Engineer will design and maintain scalable data pipelines, optimize Databricks environments, strengthen data governance, automate modern engineering workflows, and improve the reliability, performance, and cost efficiency of the organization's cloud data platform. The position will work closely with Data Strategy, Finance, Operations, Analytics, and business leadership to translate complex business needs into sustainable technical solutions. The immediate focus is enterprise data infrastructure, data warehouse development, Databricks integration, governance, and platform scalability. While advanced analytics and AI-related initiatives may become part of the organization's longer-term roadmap, this position is primarily focused on building and maturing the core cloud data foundation., Day-to-Day Responsibilities * Design, develop, deploy, and maintain scalable cloud-based data pipelines within the Databricks environment * Build and optimize enterprise ETL/ELT workflows using Python, PySpark, SQL, and modern cloud technologies * Monitor and optimize Databricks clusters to improve performance, scalability, reliability, resource utilization, and cost efficiency * Implement and maintain Delta Lake architecture to support reliable data storage, versioning, governance, and performance * Integrate Databricks with Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, Azure DevOps, and other Azure services * Develop and maintain automated CI/CD pipelines supporting data engineering deployments and workflow automation * Design monitoring and observability solutions using Databricks tools, Azure Log Analytics, and custom dashboards * Support enterprise data governance, access controls, lineage, and cataloging through Databricks Unity Catalog * Partner with Finance, Operations, Analytics, technology teams, and business stakeholders to convert requirements into scalable data solutions * Troubleshoot complex production data issues and improve platform availability, data quality, and operational stability * Help establish engineering standards, reusable frameworks, documentation, and cloud data-platform best practices * Provide technical leadership and mentorship while helping shape the long-term direction and maturity of the enterprise data ecosystem Physical and Environment Requirements This position operates primarily in a professional hybrid office environment. Physical expectations and working conditions may include sitting or standing for extended periods, working at a computer, participating in virtual and in-person meetings, and communicating regularly with technical and business stakeholders.