Senior Cloud Administrator (AWS Data Engineer)

ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS. What You’ll Do  : As a Senior Cloud Administrator at ZS you will combine strong data engineering skills with practical cloud infrastructure knowledge. The role is primarily responsible for designing, developing, supporting, and optimizing scalable data pipelines and distributed data-processing workloads. The successful candidate will work across Spark, Hadoop, SQL, Python and Linux, while supporting analytics services and the underlying AWS environment required to run them reliably, securely and cost-effectively. This is a hands-on role for a data engineer who can own complex transformations, tune jobs, troubleshoot production issues, automate recurring operational work, and collaborate with product, engineering, operations and architecture teams. The role also contributes to emerging AI-enabled data workflows through an understanding of Large Language Models, Amazon Bedrock, Agentic AI and Model Context Protocol (MCP). Design, develop, maintain and support robust data pipelines that ingest, transform and deliver data for analytics, reporting, machine learning and AI use cases. Write and review production-grade Python, PySpark and SQL for complex, high-volume data transformations. Optimize Spark and SQL workloads for performance, scalability and cost by analyzing execution plans, partitioning, skew, caching, memory pressure and I/O patterns. Build reusable ingestion and transformation components, common utilities and automation frameworks that reduce manual intervention and improve delivery consistency. Monitor scheduled pipelines and distributed jobs, investigate failures, restore services, and improve observability, alerting, and operational runbooks. Perform hands-on troubleshooting across the data-processing layer and supporting cloud infrastructure, including Linux hosts, storage, access, networking and service configuration. Conduct root cause analysis for recurring or high-impact incidents and implement long-term remediation to prevent recurrence. Apply data-engineering standards for reliability, maintainability, data quality, security and governance throughout the development lifecycle. Partner with data producers, consumers, architects, product teams and cloud operations teams to translate requirements into practical technical solutions. Contribute to technical design reviews, code reviews, release validation, capacity planning and productionreadiness assessments. Support proofs of concept involving LLMs, Amazon Bedrock, Agentic AI and MCP-based integrations where they intersect with enterprise data platforms. Work with AWS or other technology vendors and subject matter experts for advanced troubleshooting when required. What You'll Bring: 4+ years of hands-on experience in data engineering, including Apache Spark, Hadoop ecosystems and Data Warehousing. Strong expertise in designing, building, testing, and supporting scalable batch or ETL/ELT data pipelines. Hands-on expertise in Spark or PySpark development, including distributed processing concepts and job optimization. Advanced SQL skills, including complex joins, window functions, aggregations, query tuning and data validation. Strong Python skills for data processing, reusable frameworks, automation and operational tooling. In-depth hands-on experience with Linux, shell utilities, logs, processes, file systems and troubleshooting. Proven ability to implement complex data transformations, including cleansing, deduplication, enrichment, aggregation and slowly changing dimension patterns. Sound understanding of data warehousing, data lakes, partitioning, file formats, schema evolution and data quality controls. Experience diagnosing and optimizing pipeline performance, including parallelism, partition sizing, data skew, memory usage, shuffle behavior and inefficient SQL patterns. Ability to troubleshoot complex production issues, perform root cause analysis and implement sustainable corrective actions. Understanding of cloud infrastructure fundamentals relevant to analytics workloads, including compute, storage, identity and access, networking, monitoring and cost management. Understanding of Large Language Models, Amazon Bedrock, Agentic AI patterns and Model Context Protocol (MCP) concepts. Ability to collaborate with cross-functional and globally distributed teams and communicate technical findings clearly to stakeholders. Hands-on experience with AWS analytics services such as Amazon EMR, AWS Glue, Amazon Athena, Amazon SageMaker, etc. Experience with Amazon S3-based data lakes and related security, cataloging, partitioning and performance considerations. Exposure to workflow orchestration or scheduling tools and CI/CD practices for data pipelines. Familiarity with infrastructure-as-code or cloud automation tools used to provision and manage analytics environments. Knowledge of streaming data, open table formats, data governance, or metadata and catalog solutions. Experience supporting secure, regulated or mission-critical analytics platforms. Associate-level or higher AWS certification, or relevant data engineering certification Fluency in English Client-first mentality Intense work ethic Collaborative spirit and problem-solving