📍 Job Overview

Job Title: Database Engineer BI (m/w/d)
Company: Dedalus
Location: Graz, Austria (Hybrid)
Job Type: Full-Time
Category: Data Engineering
Date Posted: 2025-07-30
Experience Level: Junior/Mid (0-2 years)
Remote Status: Hybrid (Graz or Vienna office, with remote work options)

🚀 Role Summary

Collaborate with cross-functional teams to integrate and manage data from various sources into a centralized data warehouse solution.
Design, develop, and maintain OLAP cubes and ETL processes to ensure efficient data flow and analysis.
Explore new data analysis opportunities and contribute to the expansion of the data warehouse.
Optimize data performance and work on data-driven projects to improve healthcare services.

📝 Enhancement Note: This role requires strong SQL skills and familiarity with ERP systems. Experience with OLAP technology and data analysis tools is beneficial. The hybrid work arrangement allows for flexibility between working from the office and remote work.

💻 Primary Responsibilities

Data Integration & Management: Transition, validate, and homogenize data from ERP and hospital information systems into the data warehouse solution.
OLAP Cube & ETL Process Development: Design, develop, and maintain OLAP cubes and ETL processes to ensure efficient data flow and analysis.
Data Analysis & Exploration: Explore new data analysis opportunities and contribute to the expansion of the data warehouse.
Performance Optimization: Optimize data performance and work on data-driven projects to improve healthcare services.
Collaboration & Communication: Work closely with cross-functional teams, including developers, data analysts, and stakeholders, to ensure data accuracy and usability.

📝 Enhancement Note: This role requires strong analytical and problem-solving skills, as well as the ability to work independently and collaboratively. Familiarity with healthcare data and systems is beneficial but not required.

🎓 Skills & Qualifications

Education: Technical education or relevant work experience in data engineering, computer science, or a related field.

Experience: 0-2 years of experience in data engineering, data warehousing, or a related role.

Required Skills:

Strong SQL skills (Microsoft SQL Server preferred)
Experience with ERP systems (e.g., SAP)
Knowledge of OLAP technology
Analytical thinking and problem-solving skills
Ability to work independently and collaboratively

Preferred Skills:

Experience with data analysis tools (e.g., Power BI, Tableau)
Familiarity with healthcare data and systems
Knowledge of ETL tools (e.g., Talend, Pentaho)
Experience with data warehousing best practices

📝 Enhancement Note: Candidates with relevant work experience and a strong portfolio showcasing their data engineering and analysis skills will be highly sought after for this role.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Case studies demonstrating successful data integration, management, and analysis projects.
Examples of OLAP cubes and ETL processes developed, with a focus on performance and usability.
Data analysis projects showcasing innovative approaches and insights.
Performance optimization projects highlighting improved data efficiency and accuracy.

Technical Documentation:

Code comments and documentation explaining data integration, transformation, and analysis processes.
Version control and deployment processes for data warehouse solutions.
Testing methodologies and performance metrics for data analysis and optimization projects.

📝 Enhancement Note: Candidates should highlight their ability to work with large datasets, optimize data performance, and collaborate with cross-functional teams in their portfolio and application materials.

💵 Compensation & Benefits

Salary Range: The minimum salary for this position is €45,000 per year (38.5 hours) according to the IT collective agreement. An overpayment and bonus regulation based on qualifications and experience is possible.

Benefits:

Flexible working hours, including part-time work and remote work options.
Hybrid work model, with the possibility to work from home or the office.
Fresh fruit and free beverages provided.
Job ticket for public transportation in Graz or Vienna.
Group accident insurance (both professionally and privately), attractive collective agreement, and other benefits.

Working Hours: 38.5 hours per week, with flexible working hours and the possibility of working from home.

📝 Enhancement Note: The salary range provided is the minimum salary according to the IT collective agreement. Candidates with relevant experience and qualifications may negotiate a higher salary and benefits package.

🎯 Team & Company Context

🏢 Company Culture

Industry: Healthcare technology and software development.

Company Size: Medium to large (7,600 employees in more than 40 countries).

Founded: 1990 (as Dedalus Group).

Team Structure:

Data engineering team, working closely with developers, data analysts, and stakeholders.
Hybrid work environment, with offices in Graz and Vienna, and remote work options.
Collaborative and innovative company culture, focused on improving healthcare services.

Development Methodology:

Agile development methodologies, with a focus on collaboration and continuous improvement.
Regular team meetings and code reviews to ensure data accuracy and usability.
Data-driven decision-making and a focus on user experience.

Company Website: https://www.dedalusgroup.de/

📝 Enhancement Note: Dedalus is committed to providing an engaging and rewarding work experience that reflects the passion its employees bring to their mission of helping clinicians and nurses deliver better care to their served communities. The company fosters a culture where employees are encouraged to learn, innovate, and make a meaningful difference for millions of people around the world.

📈 Career & Growth Analysis

Data Engineering Career Level: Junior/Mid-level data engineer, responsible for data integration, management, and analysis. This role offers opportunities for growth and development in data engineering and related fields.

Reporting Structure: The data engineer will report directly to the data engineering team lead or manager and work closely with cross-functional teams, including developers, data analysts, and stakeholders.

Technical Impact: The data engineer will have a significant impact on the accuracy, usability, and performance of data within the healthcare organization, contributing to improved healthcare services and patient outcomes.

Growth Opportunities:

Technical Skill Development: Opportunities to develop and improve data engineering and analysis skills, with a focus on emerging technologies and best practices.
Technical Leadership: Opportunities to take on leadership roles within the data engineering team, mentoring junior team members and contributing to architectural decisions.
Career Progression: Opportunities to progress to senior data engineering roles, data architect roles, or related roles within the organization.

📝 Enhancement Note: Candidates should be prepared to take on new challenges and opportunities for growth and development within the data engineering team and the broader organization.

🌐 Work Environment

Office Type: Modern, collaborative office environments in Graz and Vienna, with remote work options.

Office Location(s): Graz and Vienna, Austria.

Workspace Context:

Collaborative workspaces with dedicated areas for data engineering, development, and analysis tasks.
Access to multiple monitors, testing devices, and other resources to support data engineering and analysis work.
Opportunities for cross-functional collaboration with developers, data analysts, and stakeholders.

Work Schedule: Flexible working hours, with the possibility of working from home or the office. The standard working week is 38.5 hours.

📝 Enhancement Note: The hybrid work arrangement allows for flexibility between working from the office and remote work, with a focus on maintaining a healthy work-life balance and supporting employee well-being.

📄 Application & Technical Interview Process

Interview Process:

Online Application Review: The hiring manager will review the candidate's application materials, including their resume, cover letter, and portfolio.
Phone or Video Screen: A brief phone or video call to discuss the candidate's qualifications, experience, and career goals.
Technical Interview: A technical interview focused on data engineering and analysis skills, with a focus on problem-solving, performance optimization, and data-driven decision-making.
Final Interview: A final interview with the hiring manager or data engineering team lead to discuss the candidate's fit within the team and the organization.

Portfolio Review Tips:

Highlight successful data integration, management, and analysis projects, with a focus on performance optimization and user experience.
Include examples of OLAP cubes and ETL processes developed, with a focus on performance and usability.
Demonstrate strong analytical thinking and problem-solving skills, with a focus on data-driven decision-making.

Technical Challenge Preparation:

Brush up on SQL skills, with a focus on Microsoft SQL Server.
Review data warehousing and analysis best practices, with a focus on performance optimization and user experience.
Prepare for problem-solving scenarios related to data integration, management, and analysis.

ATS Keywords: SQL, Data Warehousing, ETL Processes, OLAP Technology, ERP Systems, Data Analysis, Performance Optimization, Analytical Thinking, Healthcare Data, Healthcare Systems, Data-Driven Decision Making, Agile Development, Collaboration, User Experience.

📝 Enhancement Note: Candidates should tailor their application materials and prepare for the technical interview by focusing on their data engineering and analysis skills, with a focus on performance optimization and user experience.

🛠 Technology Stack & Web Infrastructure

Database Technologies:

Microsoft SQL Server (preferred)
Other SQL databases (e.g., MySQL, PostgreSQL)

ETL Tools:

Talend
Pentaho
Other ETL tools (e.g., SSIS, Informatica)

Data Analysis Tools:

Power BI
Tableau
Other data analysis tools (e.g., QlikView, Looker)

Version Control & Collaboration Tools:

Git
GitHub
Other version control and collaboration tools (e.g., SVN, Bitbucket)

📝 Enhancement Note: Candidates should have experience with SQL and familiarity with one or more ETL tools, data analysis tools, and version control systems. Experience with Microsoft SQL Server is preferred but not required.

👥 Team Culture & Values

Data Engineering Values:

Accuracy & Reliability: Ensuring data accuracy, reliability, and usability to support informed decision-making and improved healthcare services.
Performance Optimization: Continuously optimizing data performance to support efficient data analysis and reporting.
Collaboration & Communication: Working closely with cross-functional teams, including developers, data analysts, and stakeholders, to ensure data accuracy, usability, and performance.
Innovation & Continuous Learning: Staying up-to-date with emerging technologies and best practices in data engineering and analysis.

Collaboration Style:

Cross-Functional Integration: Working closely with developers, data analysts, and stakeholders to ensure data accuracy, usability, and performance.
Code Review Culture: Regular code reviews to ensure data accuracy, usability, and performance.
Knowledge Sharing: Regular team meetings and knowledge-sharing sessions to support continuous learning and skill development.

📝 Enhancement Note: Candidates should be prepared to work collaboratively with cross-functional teams, with a focus on data accuracy, usability, and performance. Strong communication and problem-solving skills are essential for success in this role.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Data Integration & Management: Transitioning, validating, and homogenizing data from ERP and hospital information systems into the data warehouse solution.
OLAP Cube & ETL Process Development: Designing, developing, and maintaining OLAP cubes and ETL processes to ensure efficient data flow and analysis.
Data Analysis & Exploration: Exploring new data analysis opportunities and contributing to the expansion of the data warehouse.
Performance Optimization: Optimizing data performance and working on data-driven projects to improve healthcare services.

Learning & Development Opportunities:

Technical Skill Development: Opportunities to develop and improve data engineering and analysis skills, with a focus on emerging technologies and best practices.
Conference Attendance & Certification: Opportunities to attend industry conferences, obtain certifications, and engage with the data engineering community.
Technical Mentorship & Leadership: Opportunities to take on leadership roles within the data engineering team, mentoring junior team members and contributing to architectural decisions.

📝 Enhancement Note: Candidates should be prepared to take on new challenges and opportunities for growth and development within the data engineering team and the broader organization. Strong problem-solving skills and a willingness to learn are essential for success in this role.

💡 Interview Preparation

Technical Questions:

Data Integration & Management: Describe your experience with data integration, validation, and homogenization. How have you ensured data accuracy and usability in previous projects?
OLAP Cube & ETL Process Development: Walk us through your experience with OLAP cube and ETL process development. How have you optimized data flow and analysis in previous projects?
Data Analysis & Exploration: Explain your approach to data analysis and exploration. How have you identified new data analysis opportunities and contributed to the expansion of the data warehouse in previous projects?
Performance Optimization: Describe your experience with data performance optimization. How have you improved data efficiency and accuracy in previous projects?

Company & Culture Questions:

Data Engineering Culture: How do you approach collaboration and communication with cross-functional teams, including developers, data analysts, and stakeholders?
Data-Driven Decision Making: How do you ensure data accuracy, usability, and performance to support informed decision-making and improved healthcare services?
User Experience: How do you consider user experience in your data engineering and analysis work? How have you ensured that data is accessible, usable, and valuable to end-users in previous projects?

Portfolio Presentation Strategy:

Data Integration & Management: Highlight successful data integration, validation, and homogenization projects, with a focus on data accuracy and usability.
OLAP Cube & ETL Process Development: Showcase OLAP cubes and ETL processes developed, with a focus on performance and usability.
Data Analysis & Exploration: Demonstrate innovative data analysis and exploration projects, with a focus on emerging technologies and best practices.
Performance Optimization: Highlight data performance optimization projects, with a focus on improved data efficiency and accuracy.

📝 Enhancement Note: Candidates should tailor their interview preparation and portfolio presentation to the specific data engineering and analysis challenges and opportunities presented by this role. Strong problem-solving skills and a willingness to learn are essential for success in this role.

📌 Application Steps

To apply for this data engineering role at Dedalus:

Prepare Your Application Materials: Tailor your resume, cover letter, and portfolio to highlight your data engineering and analysis skills, with a focus on performance optimization and user experience.
Submit Your Application: Submit your application through the application link provided in the job listing.
Prepare for the Technical Interview: Brush up on your SQL skills, review data warehousing and analysis best practices, and prepare for problem-solving scenarios related to data integration, management, and analysis.
Research the Company: Familiarize yourself with Dedalus' mission, values, and culture, and be prepared to discuss your fit within the data engineering team and the broader organization.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and data engineering industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Database Engineer BI (m/w/d)