Senior Data Warehouse / OLAP Engineer

Intetics Inc. , a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is seeking a highly skilled and experienced Senior Data Warehouse / OLAP Engineer to join our dynamic team on a full-time basis.

The client is a leading company specializing in advanced cybersecurity solutions. They provide comprehensive security measures designed to protect sensitive data and systems from cyber threats. Their offerings include vulnerability management, threat intelligence, incident response, and compliance management. With cutting-edge technology and a team of experts, they ensure robust protection for organizations across various industries, helping clients stay ahead of evolving cyber threats and maintain a secure digital environment.

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.