Data Engineer

Overview In this hands-on Data Engineer role, you will build and operate the data pipelines, lakehouse, and data products that empower decisions, analytics, and AI capabilities. You will translate architecture into production, implement scalable ELT/ETL pipelines, and establish reusable frameworks that accelerate delivery. You’ll work with Snowflake, Microsoft Fabric, and dbt to deliver governed, production-grade data assets and contribute to emerging AI-driven data workloads. This position offers growth toward technical leadership as the DAI discipline scales. ResponsibilitiesDesign, build, and maintain production-grade data pipelines across Bronze/Silver/Gold on Snowflake and Microsoft FabricImplement ELT/ETL patterns using dbt as the primary transformation layerDevelop pipelines for batch, micro-batch, and streaming ingestion from ERP/CRM/IoT sourcesTranslate architectural designs into production implementations and ensure reliability and SLA adherenceBuild and maintain lakehouse data models using Kimball, Data Vault, or OBT patternsEnforce medallion architecture and governance across data layersCreate reusable data assets and conformed dimensions for multiple analytics and AI use casesDevelop reusable engineering frameworks, templates, and patterns for fast, standardized deliveryEstablish data quality tests within dbt and pipeline stackContribute to ADRs and technical docs to scale knowledge across the teamSupport AI/ML workloads with feature pipelines and Cortex-related data feedsMentor peers, participate in design/code reviews, and assist in hiring as the team growsMaintain strong collaboration with architects, product managers, and governance leads Key requirements8-10 years of hands-on data engineering experience with Snowflake and ELT/ETL practicesProduction-grade dbt experience in enterprise environmentsExperience building data products on lakehouse architectures with medallion patternsProven track record of reusable frameworks, templates, or standards that speed deliveryExperience integrating multiple enterprise source systems and complex data integrationsMentoring or upskilling peers; readiness to grow into technical leadershipExperience with AI-adjacent data engineering (feature pipelines, Cortex, embeddings) is a plusIndustry experience in construction/building services or similar is a plusbusiness-context awarenesscollaborative mindsetownership and proactive problem-solvingSnowflake data modeling, Snowpark, dynamic tables, streams and tasks, data sharing, query optimization, cost management, security configurationMicrosoft Fabric Lakehouse, OneLake, Data Warehouse, Dataflows Gen2, Data Factory pipelines, Fabric notebooks, Eventstreamdbt model design, modular project structure, testing, documentation, incremental strategies, Semantic Layer