Role Description Legrand has an exciting opportunity for an Applied Data Analytics Intern to join the Pass and Seymour Team. This summer internship is a great opportunity for a student who enjoys working with data and wants to see how analytics is actually used inside a real business. You’ll work alongside experienced professionals on our Revenue Operations and Analytics team, supporting real initiatives using data, modeling, automation, and basic coding. Rather than fixed or predefined projects, you’ll contribute to the active queue of projects the department is working through, gaining exposure to a variety of analytics use cases over the course of the summer. This is not a classroom exercise or hypothetical case study. The work you do will support real teams and real decisions. What Will You Do? - Assisting with the development or enhancement of reports and dashboards - Supporting data modeling efforts that improve reporting and analysis - Writing or modifying basic queries or scripts to support analytics work - Exploring automation opportunities that help reduce manual processes Your responsibilities will evolve over the summer as you gain experience and confidence. Qualifications - Majors: Applied Data Science, Statistics or Statistical Modeling, Computer Science, or another quantitative or analytical field - We’re looking for a curious, motivated student who enjoys learning, problem-solving, and working with data. Required Skills: - Basic coding familiarity preferred (for example, editing simple scripts or queries) - Familiarity with Power BI and/or Power Automate is preferred Benefits - Hands-on, on-the-job training using real company data - Autonomy and flexibility to lean into your personal strengths and interests - A clear understanding of how applied data analytics creates value inside a large organization - Exposure to how data turns into insights that leaders actually use - Mentorship and collaboration with experienced professionals - Real, resume-worthy experience you can confidently talk about in interviews Company Description Legrand is the global specialist in electrical and digital building infrastructures. Our comprehensive offering of solutions for residential, commercial, and data center markets makes us a benchmark for customers worldwide. We harness technological and societal trends with lasting impacts on buildings with the purpose of improving life by transforming the spaces where people live, work, and meet with electrical and digital infrastructures and connected solutions that are simple, innovative, and sustainable. Legrand is a global, publicly traded company listed on the Euronext (Legrand SA EPA: LR). For more information, visit www.legrandgroup.com/en . Legrand, North & Central America (LNCA) is a leader in the AV, Lighting & Controls, Electrical, and Data Center markets. LNCA offers comprehensive medical, dental, and vision coverage, as well as distinctive benefits like a high employer 401K match, paid time off (PTO) and holiday pay, short-term and long-term disability benefit plans, above-benchmark paid maternity and parental leave, bonus opportunities in accordance with the Company’s incentive plans, paid time off to volunteer, and an active/growing Employee Resource Group network. For more information, visit legrand.us . Legrand’s Electrical Wiring Systems (EWS) division provides the most complete line of residential, commercial, and industrial electrical wiring devices in the world. Through commitment to innovation and quality, EWS maintains the position of industry leader in wire and cable management and wiring devices categories, specializing in raceways, flexible floor, open space and outdoor power solutions, wire mesh cable trays and systems, and designer switches and outlets. With this comprehensive offering, EWS serves a range of exciting vertical markets, including commercial office and commercial interior, data centers, education, healthcare, hospitality, manufacturing, mixed-use developments and residential. For more information, visit legrand.us/electrical wiring devices . Equal Opportunity Employer

Role Description As a Staff Engineer you will help drive architecture and technical strategy for our Data domain, spanning the entire journey from transactional source to analytical insight. You will evolve how data is leveraged across Arbor, delivering value that ensures our OLAP and AI capabilities provide a competitive edge and our OLTP foundations are built for scale. It’s a wide impacting and exciting role, crucial for Arbor's next stage of growth. We’re looking for candidates who are highly collaborative, energised by complex problems, and have a proactive approach. You will work collaboratively with multiple teams, Technical Leads, Product Directors, and Engineering Leadership to deliver strategic business objectives, all while reimagining ways of working to ensure we stay at the forefront of the industry’s AI evolution. Core Responsibilities - Define and evangelise target architectures that bridge our transactional, analytical, and AI systems, ensuring our data remains a competitive advantage. - Be a leader for our end-to-end data lifecycle, from OLTP schema design and migrations to OLAP governance, platform and analytics. - Partner with Technical Leads to provide hands-on guidance supporting feature delivery, tech debt paydown, and architecture improvements. - Collaborate with Product Directors to understand product vision and translate this into deliverables, aligning commercial and engineering goals. - Lead our shift to an AI-First way of working, architecting the data foundations to enable AI in our products and championing the latest approaches to help drive productivity gains. - Architect the future infrastructure required for LLM orchestration, Retrieval-Augmented Generation (RAG), and real-time model inference at scale. - Mentor and coach Technical Leads/Senior Engineers on architectural and engineering excellence, continually raising our bar and supporting career growth. - Act as a role model for technical leadership — work with pace, be pragmatic, and stay laser focused on outcomes not output. - Head up architectural reviews for your domain and ensure all designs meet Arbor’s quality, reliability, security and compliance standards. - Collaborate with Staff Engineers and Principal Engineers to shape Arbor’s broader technology strategy, working outside your domain as needed. - Partner with Engineering Management to help grow teams, identify and resolve systemic bottlenecks, ensuring the success of our strategic goals. Qualifications - Extensive experience overseeing data architecture and a track record of delivering iterative outcomes across one or more OLAP domains. - Strong understanding of data modelling techniques (Star, Medallion, etc.) and distributed data systems within cloud-native environments (Snowflake, etc.). - Experience in building and leveraging unstructured data across disparate systems to enable product development. - Examples of communication and influencing across technical and non-technical audiences to achieve positive outcomes. - An ability to support the growth of engineers and technical leads whilst still delivering value across distributed, cross-functional teams. - Demonstrable experience working in an ownership culture where quality, performance, observability, and security are core responsibilities for every engineer. - An understanding and opinion on how AI-first engineering will change the data ecosystem and ways of working, ideally with worked examples. - Examples of contributing to or leading technical communities of practice (e.g. chapters, guilds, architecture councils). - Practical knowledge of DataOps principles, including CI/CD for data and the orchestration of complex pipelines (e.g., AWS, Debezium, Git, Infra as Code). - Good commercial knowledge of data security standards, encryption at rest/transit, and relevant compliance laws. - Platform financial acumen - demonstrable experience of optimising SaaS platform spend, and the use of forecasting models. Bonus Skills - AI/ML Engineering & MLOps, including model deployment/serving and monitoring. - Experience with federated learning and/or privacy-enhancing technologies (PETs). - Direct experience with LLM Ops, including prompt engineering at scale, and token cost optimisation. Benefits - A dedicated wellbeing team who champion initiatives such as mindfulness, lunch n learns, manager training, mental health first aid training and much more! - 32 days holiday (plus Bank Holidays). This is made up of 25 days annual leave plus 7 extra company wide days given over Easter, Summer & Christmas. - Life Assurance paid out at 3x annual salary. - Comprehensive wellness benefit provided by AIG Smart Health, which provides a 24/7 virtual GP service, Mental health support, Counselling, and personalised Health Checks. - Private Dental Insurance with Bupa. - Salary sacrifice Pension provided by Scottish Widows. - Enhanced maternity and adoption leave (20 weeks full pay) and paternity (6 weeks full pay) pay. - 5 free return to work maternity coaching sessions, helping you adapt to this new exciting time of life! - Access to services such as Calm and Bippit (financial wellbeing coaching). - All of our roles champion flexible working and we are happy to discuss what this means to you. - Social committees that plan team, office and company wide events to bring people together and celebrate success. - Dedicated professional development training budget (CPD courses, upskilling resources, professional memberships etc). - Volunteer with a charity of your choice for a day each year. - Dog friendly offices! Interview Process - Phone screen - 1st stage - 2nd stage

Role Description We are currently looking for multiple Data Architects for one of the leading tech companies in Germany. The position is 100% remote and long-term. - Freelancing/Contracting - Start: ASAP - 100% remote - Duration: long-term 12+ months - Capacity: full-time, part-time also possible - Language: English (no German needed!!) - German business hours Requirements - Experience in data architecture, including designing scalable data platforms and data models. - Knowledge of Azure-based data platforms, including Azure Data Factory, ADLS, Synapse, Azure SQL, and Event Hubs. - Experience with Databricks, Apache Spark, and Delta Lake for modern data processing architectures. - Understanding of data modeling approaches, including dimensional, relational, and NoSQL models. - Familiarity with data governance, security, and platform reliability in enterprise environments. - Knowledge of Python, SQL, and DevOps practices such as CI/CD, Terraform, Docker, Kubernetes, or AKS. Qualifications - Please include a self-assessment (0-10, 10 = expert level) in the following areas: - Data Architecture (in general) - Azure-based data platforms - Databricks - Apache Spark - Delta Lake - Data Modeling (dimensional, relational, and NoSQL) - Data Governance & Security - Azure Data Factory - ADLS - Synapse - Azure SQL - Event Hubs - Python - SQL - DevOps - CI/CD - Terraform - Docker - Kubernetes - AKS

Role Description We are looking for a highly skilled and experienced L2 Data Engineer to join our growing Data & Analytics team. In this role, you will lead the design, development, optimization, and maintenance of scalable enterprise data platforms and cloud-native data solutions. You will work closely with architects, analysts, and business stakeholders to build high-performance data pipelines and modern lakehouse solutions that support advanced analytics, reporting, and data-driven decision-making. This opportunity is ideal for a senior data professional with strong hands-on expertise in Databricks and the Microsoft Azure ecosystem, who is passionate about building reliable, scalable, and optimized data platforms in enterprise environments. Qualifications - 5+ years of professional experience in Data Engineering or related roles. - Strong expertise in Python for enterprise data processing, transformation, and automation. - Advanced hands-on experience with Pandas, PySpark, and Spark SQL for large-scale distributed processing. - Strong experience with Databricks, including cluster management, notebook development, workflow orchestration, Delta Lake, and performance optimization. - Extensive experience building and managing enterprise data pipelines using Azure Data Factory. - Strong working knowledge of Azure Synapse Analytics, particularly Spark pool integration and enterprise data warehousing concepts. - Advanced SQL skills including query optimization, performance tuning, indexing strategies, and troubleshooting. - Strong understanding of data lake architecture, Delta Lake, incremental processing, partitioning, and lakehouse concepts. - Experience implementing data governance, security, access controls, and monitoring within cloud data platforms. - Experience handling production support, troubleshooting, and optimization of enterprise data platforms. Requirements - Design, develop, and optimize enterprise-scale data pipelines and ETL/ELT workflows using Azure and Databricks technologies. - Architect and implement scalable data ingestion, transformation, and orchestration processes using Azure Data Factory, Databricks, and Azure Synapse Analytics. - Develop high-performance data transformation frameworks using PySpark, Python, and Spark SQL for large-scale distributed data processing. - Optimize SQL queries, Spark jobs, and data workflows to improve performance, scalability, and cost efficiency. - Lead data migration initiatives, including SQL Server migrations and modernization of legacy data platforms. - Implement and maintain Delta Lake architecture, incremental data loading strategies, and enterprise data lake best practices. - Collaborate with architects and cross-functional teams to design robust and scalable data models aligned with business and governance standards. - Monitor and troubleshoot production pipelines, perform root-cause analysis, and implement preventive measures for recurring issues. - Support CI/CD implementation and infrastructure automation for data engineering workflows. - Mentor junior engineers and contribute to engineering standards, reusable frameworks, and technical best practices. - Create and maintain technical documentation including architecture diagrams, pipeline documentation, and operational runbooks. - Evaluate and recommend modern data engineering tools, frameworks, and optimization strategies. Benefits - Experience with Terraform for Azure infrastructure provisioning and Infrastructure-as-Code (IaC). - Experience implementing CI/CD pipelines for data engineering deployments. - Exposure to Lakehouse Federation, Delta Sharing, and modern data sharing architectures. - Experience with streaming and near real-time data processing solutions. - Knowledge of DevOps practices and cloud cost optimization strategies. Certification Requirement Candidates are expected to hold or be actively working toward the Databricks Certified Data Engineer Professional certification. This certification validates advanced expertise across the following domains: - Advanced ETL and ELT development using Spark SQL and PySpark - Enterprise-grade pipeline orchestration and optimization - Data modeling and scalable lakehouse architecture - Performance tuning and distributed data processing optimization - Advanced data governance and security implementation - Production-grade data engineering practices within the Databricks ecosystem