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Salary: USD 237,800 - 441,500 / annual
Veeam Software, the Data and AI Trust Company, is seeking a Senior Engineer to design and scale next-generation data processing and analytics platforms. You will build and optimize pipelines and services handling billions of records daily, enabling real-time transactions, analytical insights, and AI-driven decisioning across OLTP, OLAP, and large-scale distributed data systems.
Key Responsibilities:
- Design and implement highly scalable OLTP systems for real-time workloads and OLAP systems for complex analytical queries on massive datasets
- Build, optimize, and maintain large-scale batch and streaming pipelines using frameworks such as Apache Spark, Flink, Presto/Trino, or Kafka Streams
- Optimize systems for low-latency queries, high-throughput ingestion, and interactive analytics, ensuring seamless performance as data volumes scale to petabytes
- 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
- Ensure high availability, reliability, and monitoring across large compute and storage clusters with automated failover and recovery
- Partner with data scientists, ML engineers, and product teams to build unified, secure, and cost-efficient data platforms
Requirements:
- 6+ years of professional software engineering experience, with significant focus 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
Bonus Skills:
- 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
- Background in query engine development or contributing to open-source OLAP/OLTP frameworks
