Role Description The Finance Data Leader is accountable for driving value and impact through the effective deployment of the Global Finance Data Strategy within UK&I. This should encompass data accuracy, governance, security, and usability across local & global Finance systems and processes. The role is an enabler to major Finance transformation programmes including Horizon, and driving required adoption will be crucial, with consideration also given to other locally developed analytics required to complement Global Standard Reporting within SE Intel. These platforms must drive compliant business processes, and effective data driven decision-making. Working closely with Finance, Data Office, and Digital teams, the Finance Data Leader will own key Finance related data domains, support governance activities, and ensure business alignment to data standards and policies. The position involves a significant amount of Business Transformation, which also includes being the Unify Finance Data Stream Leader. Taking a leading position in the Rebate transformation program, including the Vistex tool deployment and other related initiatives will also be required. An expected amount of operational input will be necessary to ensure process optimisation, issue management, and continuous improvement around Finance Data awareness and associated data quality. What will you do: - Deploy Finance Data Strategy: Lead delivery of the global Finance Data Strategy across UK&I and drive adoption of standardised data and reporting. - Own Finance Data Domains: Manage key Finance data (e.g. cost centres, profit centres, GL hierarchy) ensuring accuracy, consistency, and alignment to global standards. - Drive Data Quality: Improve data quality through KPIs, dashboards, and root-cause resolution. - Lead Transformation: Drive Finance data readiness for key programmes (Horizon, Unify, Vistex) and ensure successful adoption. - Ensure Governance & Compliance: Apply data governance, security standards, and maintain clear documentation. - Enhance & Simplify Processes: Optimise Finance data processes, reduce manual effort, and partner with Digital on automation and improvements. Qualifications - Experience in Finance or Pricing - Strong understanding of SAP and data governance - Excellent stakeholder management skills - Strong ownership of data quality and governance - Collaborative and influential across diverse teams - Proactive analytical problem solver - Clear communicator with ability to simplify complexity Requirements - Experience with Schneider Electric systems and frameworks - Exposure to digital transformation or automation Benefits - Opportunity to shape and influence enterprise-wide data quality standards - Strategic role at the intersection of technology and business operations - Autonomy to drive meaningful improvements in organizational data management - Professional growth through exposure to diverse business processes - Collaborative environment fostering innovation and excellence

• As CUJU’s first Senior Data Engineer, you will build the data foundation of the company. • Your mission is to transform fragmented data across multiple systems into a reliable, scalable, and usable data layer that enables: Clear business metrics (e.g. MAU, retention) • Product decision-making • Future player performance insights • Define and implement core data models (users, events, performance) • Design and operate robust, production-grade data pipelines • Establish a single source of truth for key business and product metrics • Structure data for business and product analysis (retention, funnels, activation) • Build and deploy data services and jobs on AWS (e.g. S3, Lambda, ECS/EKS, Glue, Athena, Redshift, etc.). • Make pragmatic decisions on how data is stored, processed, and accessed • Evaluate and introduce tools (e.g. BigQuery, Snowflake, Databricks) where they add clear value • Ensure the architecture scales with product, AI, and data growth without overengineering • Optimize pipelines for scalability, cost efficiency, and performance. • Write clean, maintainable, and well-structured Python code following software engineering best practices. • Work closely with Product, Engineering, AI, and Business teams.

Role Description We are looking for a Data Engineer who enjoys solving complex problems and building scalable data solutions. This person will play a key role in designing, developing, and optimizing data platforms that support analytics, reporting, machine learning, AI initiatives, and other data-driven business needs. The ideal candidate is comfortable working on new projects, contributing to technical architecture decisions, and collaborating with cross-functional teams to deliver end-to-end solutions. Strong communication skills are important, as you'll need to explain technical concepts to both technical and non-technical audiences and work closely with clients, stakeholders, and team members. Essential Functions: - Assemble large, complex data sets that meet business requirements through extraction, transformation, and loading of data from a wide variety of data sources. - Provide operational support and troubleshooting for existing processes and systems. - Work closely with architects, solution leads, data owners, Data Scientists and key stakeholders to facilitate and coordinate the data platform backlog grooming process, triaging new feature requests in preparation for future project activities. - Deliver automation & efficient processes to ensure high quality throughput & performance of the entire data & analytics platform. - Ensure data extraction, transformation and loading data meet data security & compliance requirements. - Engage with data source platform leads to gain tactical and strategic understanding of data sources required by Agency Data Services AI/ML as well as Data Office standards. - Create data tools for data scientist team members that assist them in building and optimizing models. Qualifications - BS degree in Computer Science, Data Science, Engineering, or equivalent software/services experience required. - 4+ years working with SQL, Snowflake, Databricks, Spark, and other big data technologies. - 4+ years using Python, SQL, PySpark, R, or similar languages and manipulating, processing, and extracting value from large, disconnected data sets. - 4+ years building and optimizing data pipelines, architectures, and data sets to answer business questions and identify opportunities for improvement. - 2+ years supporting large-scale data processing and storage using Azure Data Factory, Integration Runtime, Data Lake, Databricks, Spark, Azure ML, and Cosmos DB. - 2+ years addressing privacy, compliance, and security aspects of data storage and processing; and delivering data solutions in Agile environments. - 2+ years with software development and CI/CD methodologies and tools for automated infrastructure code and MLOps and designing, implementing, and maintaining automation platforms and tools, including Ansible Tower, Azure, ARM, Terraform Enterprise, Azure DevOps, and GitHub Actions. - 2+ years with Salesforce FSC and Salesforce Data Cloud. Requirements - Strong communication and problem-solving skills. - Troubleshooting expertise. - Proficiency in Python, SQL. - Experience with Extract, Transform, Load (ETL) processes. - Experience building data pipelines. - Experience with Apache Spark and Apache Hadoop. - Experience with Amazon Web Services. Benefits - Health insurance - Paid holidays - Flexible time off - 401k retirement savings plan and company match with pre-tax and ROTH options - Dental insurance - Vision insurance - Employer paid disability insurance - Life insurance and AD&D insurance - Employee assistance program - Flexible spending accounts - Health savings account with employer contributions - Accident, critical illness, hospital indemnity, and legal assistance - Adoption assistance - Domestic partner coverage

• Design and implement ingestion and transformation pipelines from industrial source systems into clean, auditable datasets • Work directly with data from MES, SCADA, historians, PLC/OT systems, LIMS/LAB/OPS platforms, and other operational sources • Perform data quality audits and identify: Anomalies, Dead or inactive signals, Data gaps, Inconsistencies • Develop datasets and validation logic supporting KPI definitions and PoC delivery • Build PoC components such as: Batch analytics pipelines, Event detection logic, Time-series transformations • Create lightweight PoC tooling, dashboards, applications, or visualizations when required • Support presales and consulting teams by shaping technical solutions and identifying business value hidden in industrial data • Produce delivery-grade documentation, handover materials, and implementation support assets