Senior Data Warehouse / OLAP Engineer

About the project:

The only Risk-Based Vulnerability Management Platform purpose-built for the world’s most complex enterprises.

Role Overview

We are looking for a Senior OLAP / Data Warehouse Engineer to design, optimize, and maintain analytical data solutions supporting a large-scale cybersecurity platform.

The platform processes significant and continuously growing volumes of vulnerability, asset, threat, and risk-related data. The successful candidate will focus on building scalable analytical data models, improving query performance, and ensuring that data remains reliable, accessible, and performant as its volume increases.

This is a hands-on engineering role requiring a strong understanding of OLAP systems, data warehouse architecture, complex SQL, data modeling, and large-scale data processing.

Key Responsibilities

• Design, develop, and maintain scalable OLAP and data warehouse solutions.
• Create and optimize data models for reporting, analytics, and large-scale data processing.
• Design fact tables, dimension tables, aggregation layers, and analytical datasets.
• Develop efficient ETL/ELT pipelines for processing and transforming large volumes of data.
• Write, analyze, and optimize complex SQL queries.
• Review existing queries, schemas, and data-processing workflows and recommend performance improvements.
• Identify bottlenecks related to data access, transformations, storage, and query execution.
• Design appropriate partitioning, indexing, distribution, sorting, and sharding strategies.
• Ensure that analytical workloads remain performant as data volumes increase by significant factors.
• Evaluate when calculations should be performed in the database, in a processing layer, or pre-aggregated in advance.
• Improve data warehouse architecture, schema design, and storage efficiency.
• Build and maintain reusable data models and aggregation layers.
• Ensure data quality, consistency, completeness, and traceability across analytical datasets.
• Monitor pipeline performance, query execution, and data warehouse resource utilization.
• Troubleshoot production data issues and perform root-cause analysis.
• Collaborate with backend engineers, data engineers, analysts, and product stakeholders.
• Document data models, transformation logic, dependencies, and architectural decisions.

Requirements

• Minimum of 5 years of professional experience in data engineering, data warehousing, database engineering, or a similar role.
• Strong hands-on experience with OLAP systems and data warehouse architecture.
• Extensive experience working with large and continuously growing volumes of data.
• Excellent knowledge of SQL, including writing and optimizing complex analytical queries.
• Strong understanding of dimensional data modeling, including fact and dimension tables.
• Experience designing star, snowflake, or other analytical schemas.
• Experience developing and maintaining ETL/ELT pipelines.
• Strong knowledge of query execution plans and database performance optimization.
• Understanding of indexing, partitioning, sorting, data distribution, and sharding strategies.
• Experience with relational, column-oriented, or distributed analytical databases.
• Understanding of data aggregation, pre-calculation, incremental processing, and historical data management.
• Experience identifying and resolving performance and scalability issues.
• Experience maintaining data solutions on Linux platforms in cloud environments.
• Demonstrated experience with technical troubleshooting and production support.
• Ability to work independently and collaboratively with distributed teams across different time zones.
• Experience working within Agile/Scrum development environments.
• Strong written and verbal communication skills in English.

Would Be a Plus

• Experience with SingleStore/MemSQL or other distributed SQL databases.
• Experience with columnar or MPP analytical databases.
• Experience working in AWS or another major cloud environment.
• Experience with data orchestration and transformation tools.
• Experience with streaming or near-real-time data pipelines.
• Experience with data lake or lakehouse architectures.
• Experience building analytical solutions for SaaS products.
• Experience working with data analytics, business intelligence, or data science teams.
• Experience with cybersecurity, vulnerability management, or vulnerability scanning technologies such as SCA, SAST, DAST, IAST, container scanning, or VM scanning.
• Understanding of security-related datasets, including assets, vulnerabilities, threats, findings, and risk scores.