Senior Data Warehouse & OLAP Engineer for Cybersecurity

Intetics; Poland; On-site; PLN 240,000 - 360,000; Full time

Job summary

Intetics Inc. is seeking a Senior OLAP / Data Warehouse Engineer to design, optimize, and maintain analytical data solutions for a large-scale cybersecurity platform. You will build scalable data models and ETL pipelines that support reporting and analytics.

You will work with backend engineers and analysts across time zones, ensuring data quality, performance, and governance while applying best practices in SQL, dimensional modeling, and data warehouse architecture.

Qualifications

• 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.

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

Skills

OLAP systems

Data warehouse

SQL

Dimensional modeling

ETL/ELT pipelines

Query optimization

English communication

Agile/Scrum

Collaborative work

Cloud/Linux

Tools

SingleStore

Job description

Intetics Inc. is seeking a Senior OLAP / Data Warehouse Engineer to design, optimize, and maintain analytical data solutions for a large-scale cybersecurity platform. You will build scalable data models and ETL pipelines that support reporting and analytics.

You will work with backend engineers and analysts across time zones, ensuring data quality, performance, and governance while applying best practices in SQL, dimensional modeling, and data warehouse architecture.