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Senior Engineer (Hyperscale Analytics)

Veeam

Skills

Required

  • analytics
  • data architecture
  • AWS / Azure / GCP
  • BigQuery
  • Redshift
  • Spark
  • Flink
  • software engineering
  • data infrastructure
  • distributed systems
  • data lakes
  • warehouses
  • streaming platforms
  • Python / Java / Scala / Go
  • Parquet

Preferred

  • OLAP / OLTP
  • HTAP
  • Hybrid Transactional/Analytical Processing
  • containerized deployments
  • Docker
  • Kubernetes
  • cloud-native data architectures
  • Azure Synapse
  • Apache
  • Airflow

Join our team as a Senior Engineer specializing in Hyperscale Analytics. In this role, you will design and scale next-generation data processing and analytics platforms, build and optimize pipelines and services, and ensure high availability and reliability across large compute and storage clusters. You will collaborate with data scientists, ML engineers, and product teams to build unified, secure, and cost-efficient data platforms. Enjoy a range of benefits including unlimited paid time off, medical coverage, a 401(k) retirement plan, and professional training and education opportunities.

Design and implement highly scalable OLTP systems for real-time workloads and OLAP systems for complex analytical queries on massive datasets.

USD 237,800 - 441,500

Responsibilities

  • Build, optimize, and maintain large-scale batch and streaming pipelines using frameworks such as Apache Spark, Flink, Presto/Trino, or Kafka Streams.
  • Ensure high availability, reliability, and monitoring across large compute and storage clusters with automated failover and recovery.
  • Solid understanding of data modeling, ETL/ELT design, and pipeline orchestration (e.g., Airflow, dbt)

Requirements

  • 6+ years of professional software engineering experience, with a significant portion focused on data infrastructure, distributed systems, or large-scale analytics platforms
  • Strong background in cloud-scale data architecture (AWS, Azure, or GCP) — data lakes, warehouses, streaming platforms
  • Experience with modern data warehouse/lakehouse technologies (e.g., Snowflake, Databricks, BigQuery, Redshift)
  • Deep experience with distributed data processing frameworks (e.g., Spark, Flink, or similar)
  • Track record of designing for scale, reliability, and cost efficiency in production systems
  • Proficiency in one or more of: Python, Java, Scala, or Go
  • Familiarity with data lakehouse architectures and formats like Parquet, ORC, Delta, Iceberg, Hudi

Preferred

  • Experience with HTAP (Hybrid Transactional/Analytical Processing) systems or real-time analytics platforms
  • 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