Key Responsibilities Architect and build scalable, fault-tolerant data pipelines using Apache Spark and Java Lead the design and implementation of batch and streaming ETL/ELT systems handling large data volumes Perform deep performance tuning, including partitioning strategies, memory management, shuffle and skew optimization, and job cost reduction Establish coding standards and lead code and design reviews across the team Drive technical decisions related to data architecture, storage formats, and pipeline orchestration Mentor mid-level and junior engineers and serve as a technical escalation point Partner with product, analytics, and platform teams to translate requirements into scalable data systems Own production reliability, including on-call responsibilities, incident response, and root-cause analysis for pipeline failures Evaluate and introduce new tools and frameworks where they improve system performance, reliability, or maintainability Contribute to capacity planning and cost optimization for cluster infrastructure Communicate technical tradeoffs, risks, and developmental challenges effectively with technical and non-technical stakeholders Work effectively both independently and as part of a collaborative team in a changing environment Required Qualifications 7+ years of experience Bachelor's degree in Computer Science, Engineering, or a related field Master's degree in Computer Science, Engineering, or a related field Skills Java Development Apache Spark Distributed Systems SQL Data Storage Formats Cluster Managers Kafka CI/CD Containerization Infrastructure-as-Code ETL/ELT Pipeline Design Flink Data Governance Workflow Orchestration Multi-tenant Data Platform Design Technical Leadership Mentoring and Coaching Code Review Facilitation Stakeholder Communication Incident Response Coordination Capacity Planning Cross-team Dependency Management Schedule Start date: 2026-09-23