Senior Data Warehouse, OLAP Engineer

🔥 0 minutes ago

🌐 Armenia, Poland, +4 more countries – Remote

🇦🇲 Armenia – Remote 🇵🇱 Poland – Remote 🇷🇸 Serbia – Remote 🇷🇴 Romania – Remote 🇱🇻 Latvia – Remote 🇦🇿 Azerbaijan – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Intetics

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 1995

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Intetics is an innovative company providing custom software development services, specializing in AI and machine learning solutions. They offer Remote In-Sourcing® to build expert teams for software engineering and data processing projects, along with advanced tools like TETRA™ for software quality assessment. Intetics aims to empower businesses by leveraging high-quality data and integrating modern technologies across various industries, including healthcare, finance, and more.

📋 Description

• 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 partitioning, indexing, distribution, sorting, and sharding strategies. • Ensure analytical workloads remain performant as data volumes increase. • Evaluate whether calculations belong in the database, 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. • Plus: Experience with SingleStore/MemSQL or other distributed SQL databases. • Plus: Experience with columnar or MPP analytical databases. • Plus: Experience working in AWS or another major cloud environment. • Plus: Experience with data orchestration and transformation tools. • Plus: Experience with streaming or near-real-time data pipelines. • Plus: Experience with data lake or lakehouse architectures. • Plus: Experience building analytical solutions for SaaS products. • Plus: Experience working with data analytics, business intelligence, or data science teams. • Plus: Experience with cybersecurity, vulnerability management, or vulnerability scanning technologies such as SCA, SAST, DAST, IAST, container scanning, or VM scanning. • Plus: Understanding of security-related datasets, including assets, vulnerabilities, threats, findings, and risk scores.