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# Senior Engineer - Hyperscale Analytics

- **Company:** Veeam Software Corporation
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $234,840.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, BigQuery, Software Code Optimization, Cyber Security, Data Architecture, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Warehousing, Distributed Computing Environment, Distributed Data Store, Distributed Systems, Hadoop Distributed File System, Python (Programming Language), Online Analytical Processing, Online Transaction Processing, Open Source Technology, Cloud Services, Scala (Programming Language), Security Information and Event Management, Software Engineering, Data Streaming, Parquet, Transaction Processing (Computing), Data Processing, System Availability, Snowflake, Apache Spark, Containerization, Data Lakes, Kubernetes, Apache Flink, Cassandra, Apache Kafka, Data Management, Presto, Data Lakehouse, Azure Synapse Analytics, Stream Analytics, Data Pipelines, Docker, Amazon Redshift, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-engineer-hyperscale-analytics-san-jose-ca--75f887d2-7698-413b-9ad7-6a61dbb1545a

## About the Role

Risk, Artificial Intelligence (AI), Information/Data Security (InfoSec), Security Information and Event Management (SIEM), Analysis Skills, Transaction Processing/Management, Data Processing, OLAP (OnLine Analytical Processing), Large-Scale Systems, Distributed Computing, * 6+ years of professional software engineering experience, with a significant portion focused on data infrastructure, distributed systems, or large-scale analytics platforms

 * Deep experience with distributed data processing frameworks (e.g., Spark, Flink, or similar)
 * Strong background in cloud-scale data architecture (AWS, Azure, or GCP) - data lakes, warehouses, streaming platforms
 * Proficiency in one or more of: Python, Java, Scala, or Go
 * Experience with modern data warehouse/lakehouse technologies (e.g., Snowflake, Databricks, BigQuery, Redshift)
 * Solid understanding of data modeling, ETL/ELT design, and pipeline orchestration (e.g., Airflow, dbt)
 * Track record of designing for scale, reliability, and cost efficiency in production systems, * Experience with HTAP (Hybrid Transactional/Analytical Processing) systems or real-time analytics platforms
 * Familiarity with data lakehouse architectures and formats like Parquet, ORC, Delta, Iceberg, Hudi
 * Knowledge of containerized deployments (Docker, Kubernetes) and cloud-native data architectures (AWS Redshift, GCP BigQuery, Azure Synapse)
 * Background in query engine development or contributing to open-source OLAP/OLTP frameworks

## Description

We are seeking exceptional Senior Engineer- Hyperscale Analytics to design and scale next-generation data processing and analytics platforms that power OLTP, OLAP, and large-scale distributed data systems. You will build and optimize pipelines and services that handle billions of records daily, enabling real-time transactions, analytical insights, and AI-driven decisioning., * Transactional & Analytical Systems: Design and implement highly scalable OLTP systems for real-time workloads and OLAP systems for complex analytical queries on massive datasets

 * Distributed Processing: Build, optimize, and maintain large-scale batch and streaming pipelines using frameworks such as Apache Spark, Flink, Presto/Trino, or Kafka Streams
 * System Performance & Scale: Optimize systems for low-latency queries, high-throughput ingestion, and interactive analytics, ensuring seamless performance as data volumes scale to petabytes
 * Data Infrastructure: Develop and integrate with modern storage and processing systems (e.g., Snowflake, BigQuery, Redshift, Cassandra, HDFS, Delta Lake, Iceberg) to support hybrid analytical/transactional workloads
 * Reliability & Observability: Ensure high availability, reliability, and monitoring across large compute and storage clusters with automated failover and recovery
 * Collaboration: Partner with data scientists, ML engineers, and product teams to build unified, secure, and cost-efficient data platforms