Senior Hyperscale Analytics Engineer - Real-Time Data at Scale

Veeam Software; San Jose (CA); On-site; USD 238,000 - 442,000; Full time

Benefits offered by this job

Unlimited PTO

Holidays and wellbeing benefits

Medical/dental/vision coverage

401(k) plan

Parental leave

Job summary

Veeam Software is seeking a Senior Engineer for Hyperscale Analytics to design and scale data processing platforms handling OLTP, OLAP, and billions of records daily. You will build pipelines with Spark, Flink, Presto/Trino, and Kafka Streams to enable real-time transactions and AI-driven insights.

You will work on data infrastructure, modern storage and processing systems, and collaborate with data scientists and ML engineers to ensure reliability, cost efficiency, and observability at petabyte

Qualifications

• 6+ years of professional software engineering experience with data infrastructure or large-scale analytics platforms.
• Experience with distributed data processing frameworks (Spark, Flink, or similar).
• Strong background in cloud-scale data architecture (AWS, Azure, or GCP) and data lakes/warehouses.

Responsibilities

• Design and implement highly scalable OLTP and OLAP systems for real-time and analytics workloads.
• Build and optimize large-scale batch and streaming pipelines (Spark, Flink, Kafka Streams).
• Ensure low-latency queries, high-throughput ingestion, and secure, observable data platforms.

Skills

Distributed systems

Data infrastructure

Cloud data architecture

Programming languages

ETL/ELT design

Data modeling

Tools

Apache Spark

Apache Flink

Presto/Trino

Kafka Streams

Snowflake

BigQuery

Redshift

Airflow

dbt

Docker

Kubernetes

Job description

Veeam Software is seeking a Senior Engineer for Hyperscale Analytics to design and scale data processing platforms handling OLTP, OLAP, and billions of records daily. You will build pipelines with Spark, Flink, Presto/Trino, and Kafka Streams to enable real-time transactions and AI-driven insights.

You will work on data infrastructure, modern storage and processing systems, and collaborate with data scientists and ML engineers to ensure reliability, cost efficiency, and observability at petabyte