Digital Reality Operations Analyst

🏢 Summary: The role focuses on improving data governance, standardising reporting, and automating workflows to enhance commercial decision-making across regions. It ensures consistent KPI definitions, reliable reporting, and early detection of data issues by aligning business and IT. The position drives scalable analytics solutions and AI-assisted insights within a multinational B2B manufacturing environment. 🗂️ Requirements: 5+ years in similar data/BI role in B2B multinational manufacturing, Hands-on experience with BI tools (Power BI or Tableau), Strong SQL and Python skills, Experience with ERP/CRM systems (SAP, Oracle, Salesforce), Proven experience in reporting standardisation and automation, Ability to define data quality standards and validation rules, Experience coordinating UAT and translating business needs into technical requirements 📃 Skills: PowerBI, Tableau, SQL, Python, SAP, Oracle, Salesforce, ERP, CRM, AI, UAT, DataGovernance, Automation, DataValidation 🏢 Description: This position strengthens commercial decision-making by making reporting trustworthy, consistent, and scalable. You’ll improve how data is governed, reduce reporting noise, and replace manual workflows with automation—working closely with both business stakeholders and IT. Your mission: Teams across regions use the same KPI definitions and reporting logic Data issues are caught early through clear validation standards and monitoring Reporting becomes faster and more reliable thanks to automation Business and IT move in sync on data improvements and system changes What you’ll own: 1) Data quality & governance Set practical rules for data quality (validation checks, standards, documentation) Investigate mismatches between systems/reports and coordinate fixes Build and maintain “how we work with data” documentation (SOPs, definitions) 2) Reporting clean-up & standardisation Align reporting across regions: KPI definitions, structures, and datasets Identify overlapping dashboards and remove duplicates Maintain a clear map of reporting assets and their data sources 3) Automation & modern analytics Spot repetitive/manual reporting tasks and turn them into automated workflows Define requirements and measurable outcomes for automation initiatives Ensure solutions are maintainable, documented, and adopted by the team Use AI-assisted approaches to speed up insights (trend/anomaly spotting, interpretation support) 4) Business–IT delivery Translate commercial needs into data/reporting requirements Support planning and prioritisation of data initiatives Coordinate UAT by identifying the right users and guiding validation What we’re looking for: 5+ years in a similar role within a B2B multinational manufacturing environment Strong hands-on skills with BI (Power BI / Tableau) and data tools (SQL, Python) Solid understanding of ERP/CRM ecosystems (e.g., SAP, Oracle, Salesforce) Experience improving reporting through automation and better visualisation practices Strong stakeholder management: able to influence without formal authority High attention to detail, good organisation, and ability to work across time zones Proactive mindset and interest in new technologies/automation

🏢 Summary: The offer is for a Data Engineer/DevOps professional to build and maintain scalable data platforms and cloud infrastructure, automate data workflows using CI/CD and Infrastructure as Code, and optimize data pipelines for high availability and security. The role focuses on managing data processing environments and supporting model deployment in collaboration with data teams. 🗂️ Requirements: Proficiency in Linux/Unix administration, Strong Shell scripting skills, Advanced knowledge of Python, Advanced knowledge of SQL, 3–5 years experience in Data Engineering or DevOps, Degree in Computer Science, Data Engineering, or related field 📃 Skills: Linux, Unix, Shell, Python, SQL, Azure, Spark, Databricks, Docker, Kubernetes, Terraform, Ansible, Hadoop, Kafka, CICD 🏢 Description: Key Responsibilities Infrastructure Management: Build and maintain scalable data platforms and cloud-based infrastructure. Automation: Implement CI/CD pipelines for data workflows and automate manual processes using Infrastructure as Code (IaC). Pipeline Optimization: Monitor, troubleshoot, and optimize data processing jobs to ensure high availability. Security & Compliance: Ensure data integrity and security across all environments. Collaboration: Work closely with Data Scientists and Engineers to streamline the deployment of data models. Requirements Systems & Scripting: Strong proficiency in Linux/Unix administration and Shell scripting. Programming: Advanced knowledge of Python and SQL . Mindset: Exceptional analytical and problem-solving skills. Education: Degree in Computer Science, Data Engineering, or a related technical field. Experience: 3–5 years of proven experience in Data Engineering or a DevOps-related role. Nice-to-have Cloud & Big Data: Hands-on experience with Azure , Apache Spark , and Databricks . DevOps Tools: Proficiency with Docker , Kubernetes , CI/CD tools, and IaC (e.g., Terraform, Ansible). Data Ecosystems: Familiarity with Big Data technologies such as Hadoop or Kafka .

🏢 Summary: The role involves full ownership of software products, covering architecture design, development, deployment, and maintenance within distributed systems. It focuses on building RESTful microservices, automation tools, and modular components to industrialize asset management processes. The position also ensures system integration, cloud-native deployment, and ongoing optimization in a DevOps environment. 🗂️ Requirements: Advanced proficiency in Python, Experience building RESTful microservices, Experience with ETL data processing, Basic knowledge of Java, Foundational understanding of Spring Boot, Proficiency in PostgreSQL design and optimization, Familiarity with S3 or NAS storage, Experience with Kubernetes and Docker, Experience with CI/CD pipelines, Working knowledge of Linux environments 📃 Skills: Python, Java, Spring, PostgreSQL, S3, NAS, Kubernetes, Docker, Jenkins, BitBucket, Linux, ETL, REST 🏢 Description: Key Responsibilities: End-to-End Product Ownership: Manages the full software lifecycle, from initial concept and architecture design to production release and maintenance. Workflow Automation: Develops internal tools to automate data collection, remediation, and reporting to industrialize asset management processes. Modular Development: Delivers independent, high-quality software modules through iterative cycles of coding, testing, and documentation. System Integration: Ensures seamless integration and maintenance of applications within complex, distributed infrastructures. Technical Collaboration: Works closely with architects and product owners to define technical solutions and security standards. Technical Requirements: Python Expertise: Advanced proficiency in Python for building RESTful microservices, automation scripts, and ETL data processing. Basic Java Knowledge: Foundational understanding of Java and Spring Boot to support existing backend components and minor maintenance tasks. Data & Storage: Proficiency in PostgreSQL (design and optimization) and familiarity with S3 or NAS storage solutions. Cloud & DevOps: Familiarity with Kubernetes, Docker, and CI/CD pipelines (Jenkins, BitBucket) within a Linux environment.