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# TELECOMMUTE Lead Data Software Engineer

- **Company:** Databricks
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Business Analytics Applications, Data Analysis, Microsoft Azure, Big Data, Code Review, Continuous Integration, Data Architecture, Data Governance, Data Infrastructure, Data Transformation, Data Visualization, Relational Databases, Python (Programming Language), NoSQL, NumPy, Performance Tuning, Power BI, Software Construction, SQL Databases, Tableau (Software), Workflow Management Systems, Data Processing, Data Storage Technologies, Cloud Platform System, System Availability, Apache Spark, Git, Pandas, Data Lakes, Pyspark, Git Flow, AWS Glue, Performance Monitor, Looker Analytics, Software Version Control, Databricks
- **Published:** August 14, 2026
- **Apply:** https://www.dice.com/job-detail/5fb8a567-914f-45f4-afa8-72a9176f6858

## About the Role

document, and enforce architectural principles, technical standards, and best practices for traceability solutions Oversee the design of robust pipelines, quality controls, and proactive monitoring frameworks to ensure data availability, quality, integrity, and timeliness Guide and support data engineers and technical contributors on implementation choices, troubleshooting, performance optimization, and best practices Contribute strategically to Run execution and continuous improvement, including data quality, lineage, auditability, and operational monitoring Translate traceability and regulatory requirements into technical solutions, data models, and platform capabilities Collaborate closely with solution architects, data platform teams, and external partners to ensure alignment and scalability Support adoption of traceability solutions by business and sustainability teams through technical guidance and enablement Requirements 5+ years of experience in technical roles within data,
analytics, or platform environments, with exposure to leadership or solution ownership responsibilities Expertise in cloud-based data platforms, preferably Azure Background in data architecture, data modeling, and data platform design, including complex, large-scale datasets Hands-on experience with Databricks, Spark/PySpark, and SQL for data transformation pipelines and analytics platforms Proficiency in SQL and data storage concepts, including relational databases, NoSQL, and data lakes, with performance optimization for large and complex datasets Knowledge of Python and data manipulation ecosystems such as Pandas and NumPy, with ability to review and guide code even if not hands-on full time Familiarity with data visualization tools including Power BI, Tableau, and Looker Experience designing or governing data quality frameworks, lineage, monitoring, and auditability Familiarity with orchestration tools and data integration patterns such as ADF, Airflow, and AWS Glue, or equivalent
Experience with software engineering best practices including version control with git, branching strategies, code reviews, and CI/CD Understanding of data governance, security, and access control for sensitive business and regulatory data Proficiency in English at a B2+ level



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