Senior Data Warehouse / OLAP Engineer
New
I
Intetics Inc.Cybersecurity
Armenia. Poland. Serbia. Romania. Latvia. AzerbaijanFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- Minimum of 5 years
- Required Skills
- SQLCloud ComputingLinuxData modeling
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.
- Experience with relational, column-oriented, or distributed analytical databases.
- Experience maintaining data solutions on Linux platforms in cloud environments.
- 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 data quality, consistency, completeness, and traceability across analytical datasets.
- Troubleshoot production data issues and perform root-cause analysis.
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