Top Tech Jobs & Startup Jobs

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.

The Data Engineer will design and build data pipelines, integrate data from various sources, optimize workflows, and guide data strategies, supporting project management and best practices.

The Senior AI Engineer will lead ML project implementations, manage client engagements, optimize Generative AI and NLP applications, and ensure production-grade ML solutions.

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.

Design and present scalable Databricks Lakehouse architectures, lead data engineering and ML projects, build reference architectures and demos, validate cloud integrations, and support sales through technical account planning and customer engagement.

Design and maintain robust CI/CD pipelines, manage integrations with large customer repositories, develop full-stack software, provision cloud infra with Terraform, and implement scalable data pipelines. Learn and apply PySpark, modern data architecture, and AI/agentic tools (Claude, Cursor) to automate and accelerate engineering workflows.

Work directly with clients to design, build, and deploy end-to-end production software and AI solutions. Lead solution architecture across backend, frontend, data pipelines, and ML models; develop production-grade applications; engage stakeholders from engineers to executives; and contribute reusable frameworks and best practices.