TELECOMMUTE Lead Data Software Engineer

Databricks
United States
6 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Languages
English
Job source

Tech stack

Airflow Business Analytics Applications Data Analysis Microsoft Azure Big Data Code Review Continuous Integration Data Architecture Data Governance Data Infrastructure Data Transformation Data Visualization
+25 more
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

Requirements

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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