Senior Data Warehouse / OLAP Engineer

Intetics Inc. is a leading American technology company providing custom software application development, distributed professional teams creation, software product quality assessment, and “all-things-digital” solutions built with SMAC, RPA, AI/ML, IoT, blockchain, and GIS/UAV/LBS technologies.

The client is a leading company specializing in advanced cybersecurity solutions, offering vulnerability management, threat intelligence, incident response, and compliance management to protect sensitive data and systems.

• Minimum of 5 years of experience in data engineering, data warehousing, or database engineering.
• Strong hands-on experience with OLAP systems and data warehouse architecture.
• Extensive experience with large and continuously growing data volumes.
• 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 time zones.
• Experience working within Agile/Scrum environments.
• Strong written and verbal communication skills in English.

Nice to have:

• 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 collaborating 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.

• 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 analytical workloads remain performant as data volumes increase significantly.
• Evaluate when calculations should be performed in the database, processing layer, or pre-aggregated.
• 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 datasets.
• Monitor pipeline performance, query execution, and 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.