Role Description Legrand has an exciting opportunity for an Azure Data Engineer to join our Information Technology Team. This is a remote position. - Lead the design, development, and optimization of enterprise data platforms on Azure. - Advance our data modernization strategy, including Azure Fabric adoption, data lakehouse architecture, and integration of enterprise systems such as ERP, MDM, and analytics platforms. - Work closely with business, analytics, and governance teams to deliver scalable, high-performance data solutions that enable trusted, data-driven decision-making. Benefits - Comprehensive medical, dental, and vision coverage. - 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. - Active/growing Employee Resource Group network. 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 .

• Providing the organization’s data consumers high quality data sets by data curation, consolidation, and manipulation from a wide variety of large scale (terabyte and growing) sources. • Building high quality data pipelines and ETL processes that interact with terabytes of data on leading platforms such as Snowflake and BigQuery. • Developing and improving our enterprise data marts by creating efficient and scalable data models to be used across the organization. • Partnering with our analytics, data science, crm, and machine learning teams for data solutions. • Responsible for an enterprise data mart integrity, validation, and documentation. • Responsible for the data pipelines’ SLA and dependency management. • Writing technical documentation for data solutions, and presenting at design reviews • Solving data pipeline failure events and implementing sound anomaly detection • Working with various teams from analytics to product owners and front end developers on tracking solutions and solving technical challenges • Leading medium to large size projects in terms of writing technical specs and project planning • Mentoring junior members of the team.

Role Description This role is for an experienced, hands‑on practitioner capable of designing and implementing performant and scalable data solutions, and optimizing data flow. Qualifications - 5+ years of industry experience as a hands‑on practitioner in Data Engineering. - Advanced proficiency in Python and SQL (TypeScript/JavaScript/Java a plus). - Proven experience with ETL/ELT implementation and data modeling for data warehouses/lakehouses. - Understanding and experience with the Medallion Architecture (Bronze, Silver, and Gold layers). - Experience with advanced DevOps, CI/CD, and strong Linux system administration skills. - Expertise in 2–3 major cloud and data platforms (e.g., Azure, AWS, GCP, Snowflake, Databricks, and Fabric). Requirements - Design, build, and deploy robust data platforms, including data lakehouses and warehouses, and automate their operation using tools like Azure DevOps, Terraform, and CloudFormation. - Implement and optimize advanced ETL/ELT data pipelines and orchestrate complex data jobs using platforms like Databricks notebooks and dbt. - Design and maintain data models, potentially utilizing data virtualization tools like Cube. - Leverage managed and serverless cloud offerings to create performant and scalable cloud‑native application solutions and data pipelines. - Apply best practices for data security and ensure compliance with governance requirements. - Build high‑performance data pipelines and integrate with BI tools like Tableau, Looker, and Power BI. - Provide leadership in applying software development best practices, including CI/CD and managing Infrastructure as Code. Benefits - Competitive base compensation with performance bonuses. - Comprehensive health, dental, and vision benefits. - 401(k) with company match. - Work on diverse, high-impact engagements across industries. - Direct access to leading-edge data and AI stacks (Snowflake, Databricks, AWS, GCP, Azure). - Builder culture where analysts lead engagements and ship work, not slides. - Professional development budget and certification support. - Flexible remote work environment with a collaborative, high-talent team.

• Own the Data Layer end to end: ingestion from file-, event-, and API-based sources; the medallion-style model (raw → refined → curated); and the serving layer that powers the product and the AI brain. • Build and operate the ingestion and transformation pipelines that power the Data Layer, using a modern orchestration framework and cloud data warehouse. • Ingest and reconcile large, messy, real-world data across many source types and shapes — batch files, streaming events, and APIs. • Model data across medallion layers so it's trustworthy, queryable, and stable for downstream teams and the AI. • Help take the Data Layer to the next level — better architecture, better tooling, more scale, more sources — and have a real say in what that looks like. • Operate AI coding agents (Claude Code and similar) at a high level: scope work, structure context, run agents in parallel where it makes sense, and ship reviewed, production-quality output. • Build the systems that make data trustworthy — validation, reconciliation, lineage, backfills, idempotent and incremental loads — so downstream teams and the AI don't inherit silent errors. • Partner with backend, AI, and product engineers (and occasionally customers' IT teams) to define the data contracts they build on.