Discover and apply for elite Spark (Java/Scala & Hadoop) - VP jobs, a senior leadership role at the intersection of big data engineering and strategic management. Professionals in this high-impact position are responsible for architecting, leading, and scaling enterprise-grade data processing platforms that drive business intelligence, advanced analytics, and machine learning initiatives. As a Vice President, you will transcend hands-on coding to provide technical vision, oversee substantial engineering teams, and align data infrastructure with core business objectives to unlock transformative value from massive datasets. Typical responsibilities for this executive role encompass setting the technical direction for the organization's big data stack, with Apache Spark as the core computational engine. Leaders in these jobs are accountable for the entire data pipeline lifecycle, from ingestion through processing to delivery, ensuring reliability, performance, and cost-efficiency. A key duty involves managing and mentoring large teams of data engineers, data scientists, and platform developers, fostering a culture of innovation and engineering excellence. Furthermore, VPs in this domain collaborate closely with C-suite executives, product managers, and business stakeholders to translate complex business challenges into robust data-driven solutions, requiring a blend of deep technical expertise and sharp business acumen. The typical skill set and requirements for Spark (Java/Scala & Hadoop) - VP jobs are rigorous. Candidates must possess profound, hands-on expertise in distributed computing frameworks, specifically Apache Spark, with strong programming proficiency in either Java or Scala. In-depth knowledge of the Hadoop ecosystem (HDFS, YARN, Hive, etc.) and related cloud data services (AWS EMR, Azure Databricks, Google Cloud DataProc) is essential. Beyond pure technology, successful professionals demonstrate a proven track record in strategic planning, budgetary oversight, and project delivery at an enterprise scale. Exceptional leadership, communication, and stakeholder management skills are non-negotiable, as is a deep understanding of data architecture patterns, real-time processing, and data governance principles. These executive jobs typically require extensive experience, often a decade or more, in data engineering with progressive leadership responsibility, culminating in a role that shapes the organization's data destiny.
