Full Lifecycle Data Engineer

Your Responsibilities We are looking for experienced Full-Lifecycle Data Engineers to design, build, and operate our next-generation data platform. This role owns the full data lifecycle—from ingestion and transformation through modeling, serving, observability, and production operations. Successful candidates combine software engineering, data engineering, and analytics engineering skills to deliver reliable, scalable data products that power analytics, applications, and machine learning. Key Responsibilities Data Ingestion & Integration Build and maintain scalable batch and streaming data pipelines Integrate data from APIs, event streams, databases, SaaS tools, and third-party systems Ensure reliable, fault-tolerant ingestion across multiple sources Data Processing & Transformation Design and implement transformation pipelines using ELT/ETL patterns Develop modular, reusable data transformations (Databricks experience a plus) Ensure data consistency, correctness, and reproducibility Data Storage & Modeling Design and maintain data warehouses, lakes, and lakehouse architectures Build analytics-ready data models (star schema, wide tables, semantic layers) Optimize data structures for performance and cost efficiency Data Products & Serving Layer Build data services or APIs that expose curated datasets to downstream consumers Enable self-serve analytics via BI tools and semantic modeling layers Support embedded analytics or product-facing data features when needed Orchestration & Reliability Own scheduling and orchestration systems Implement monitoring, alerting, and data observability practices Debug and resolve end-to-end data issues across the stack Collaboration & Enablement Partner with analytics, product, and engineering teams to define data needs Translate business requirements into scalable data solutions Support experimentation, reporting, and machine learning workflows Qualifications Strong programming skills AdvancedSQLand data modeling expertise Experience with data processing frameworks Hands-on experience with cloud platforms (Azure) Experience with modern data warehouses Familiarity with orchestration tools Understanding of distributed systems and data architecture patterns Ability to build both backend-style data systems and analytics pipelines Nice to Have Experience buildingdata APIs or internal data services Infrastructure-as-code CI/CD for data systems Experience with ML data pipelines or feature stores Front-end exposure for dashboards or internal tools Data governance enablement Security and privacy (HIPAA) Soft Skills Comfortable owning ambiguous, end-to-end problems Clear communication with both engineers and business users #J-18808-Ljbffr Lockton