Top Tech Jobs & Startup Jobs

Design, build, and maintain end-to-end data pipelines and ETL/ELT processes on Databricks (Azure). Implement Spark (PySpark/Scala) transformations, manage Delta Lake/Unity Catalog, integrate varied data sources (APIs, databases, IoT), and support analytics via APIs and data services. Ensure data quality, security, CI/CD, and performance optimization while collaborating with cross-functional teams to deliver scalable BI and reporting solutions.

Lead design and delivery of enterprise Databricks Lakehouse architectures, implement and optimize large-scale Spark data pipelines, advise clients on governance, security, MLOps and cloud integration, and provide workshops and mentorship to accelerate adoption and operationalize AI at scale.

Design, build, and deploy end-to-end data pipelines and ETL/ELT solutions on Databricks. Integrate diverse data sources, implement Spark (PySpark/Scala) transformations, optimize SQL queries, maintain data models, build APIs/data services, ensure data quality/security, monitor/optimize pipeline performance in cloud environments, and follow CI/CD and governance practices.

Manage allocation of Databricks consultants across geographies, maintaining high utilization, updating Salesforce and company tools, producing executive reports, and coordinating with Finance, Recruitment, and Delivery while supporting EST and PST time zones.

Develop Generative AI solutions, build predictive models, implement ML pipelines, and collaborate with teams to create scalable AI/ML applications.

The Technical Project Manager oversees technology projects, managing cloud data engagements, setting timelines, delegating tasks, and ensuring adherence to project metrics. They lead cross-functional teams, monitor progress, and implement risk mitigation strategies, while staying up-to-date with industry trends.

Lead the design and execution of migrating legacy databases to Databricks, focusing on architecture, performance optimization, pipeline engineering, and governance. Collaborate with teams to ensure best practices and knowledge transfer in data platform development.

Lead end-to-end customer engagements to architect, implement, and optimize large-scale Spark/Databricks workloads. Troubleshoot and tune PySpark and DBSQL jobs, design PoCs and workshops, advocate strategically for customers, collaborate cross-functionally, and mentor junior engineers on Lakehouse best practices.

Lead customer engagements to architect, implement, and optimize large-scale Spark and Databricks workloads. Troubleshoot and tune distributed PySpark and DBSQL jobs, design PoCs/workshops, advocate technical solutions, mentor junior engineers, and collaborate cross-functionally to improve platform features and cost-efficiency.

Lead professional services projects to design, build, and deploy production-grade Databricks Big Data and AI solutions. Write production Python/Scala code, optimize Apache Spark workloads, design CI/CD and MLOps pipelines, advise on architecture and build vs buy decisions, and resolve complex performance issues while collaborating with product engineering.