Software Engineer

Process Mining Data Engineering Consultant

Key Responsibilities:

• Execute process mining initiatives by monitor, support, developing, maintaining, and optimizing data pipelines, models, dashboards, and execution apps
• Implement structured knowledge layers, ensuring seamless integration of business context into process insights.
• Translate business requirements into detailed technical implementations aligned with global best practices.
• Develop and maintain high-quality data extraction, transformation, and integration pipelines, ensuring accuracy, consistency, and reliability.
• Troubleshoot, optimize, and maintain process mining solutions, ensuring efficient execution and timely delivery of insights.
• Support the implementation of AI-driven techniques (e.g., predictive analytics, NLP, machine learning) to enhance automation and decision-making in process mining.
• Adhere to DSO ways of working and agile development methodologies, participating in sprints, backlog refinement, and collaborative development efforts.
• Ensure compliance with global data governance and security standards while integrating enterprise applications and external data sources into process mining solutions.
• Support knowledge transfer initiatives by maintaining documentation, training materials, and best practices repositories.

Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Business Administration, Mathematics, or a related field.
• 5+ years of experience in process improvement, technology consulting, or software implementation, with a strong focus on data engineering, process mining, and automation.
• Hands-on experience in different business domain (e.g., Finance, Procurement, Supply Chain, HR, or Customer Service) with an understanding of process workflows, KPIs, and optimization opportunities

Technical Skills:

• Process Mining Expertise: Hands-on experience with leading process mining platforms (e.g., Celonis, Signavio, Power Automate), with a focus on implementing dashboards, execution apps, and automation workflows.
• Data Engineering & Integration: Familiarity with data integration techniques and tools, including ETL (Extract, Transform, Load) processes and data warehousing concepts.
• Enterprise Applications & Data Structures: Familiarity with business applications such as SAP (S/4HANA, ECC), Ariba, ServiceNow, Salesforce, including data structures, tables, and integration points.
• Cloud & Big Data Processing: Experience with cloud platforms (e.g., AWS, Google Cloud, Azure, Salesforce Data Cloud) and Databricks for large-scale data processing and analytics.
• Knowledge Layers & Graphs: Exposure to Knowledge Layers/Knowledge Graphs and tools like Neo4j, Amazon Neptune, Stardog for structured business context integration.
• AI-Driven Techniques: Experience supporting AI-powered process mining solutions, including predictive analytics, NLP, and machine learning.
• Data Analytics Skills: Strong capabilities in data analytics, including experience with SQL, Python, or R for data manipulation and analysis.
• BI & Visualization: Proficiency in Power BI, Tableau, or similar tools for effective data visualization and reporting.
• Version Control & Collaboration: Experience using GitHub for version control, collaboration, and CI/CD processes.
• Automation Technologies: Knowledge of automation tools and technologies, such as Robotic Process Automation (RPA), and experience in identifying automation opportunities.