Senior Engineer - Hyperscale Analytics

About the role

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

About the Role

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.

What You’ll Do

- 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

What You’ll Bring

- 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