lemlist

Data Engineer

• Design, build, and improve a scalable data platform supporting product data solutions
• Collaborate with product and business teams to build scalable and agile solutions
• Define technical standards and contribute to data platform architecture and deployment decisions based on the strategic product roadmap
• Develop, deploy, and manage efficient data platforms and automated data pipelines using cloud-based and on-premise technologies
• Design, maintain, and enhance key data product features for quality, certification, accessibility, and integration
• Analyze and develop data operations and pipelines following enterprise guidelines and best practices
• Apply data quality processes, governance, and catalog/glossary curation
• Maintain and improve existing pipelines by integrating new features and change requests using an agile approach
• Ensure data quality, lineage, versioning, and observability across the stack
• Support CI/CD and release processes
• Onboard the existing data platform end to end, including sources, ingestion jobs, warehouse models, orchestration, BI layer, and consumers
• Audit the current stack, identify risks and gaps, and estimate their costs
• Deliver a quick win such as fixing an unreliable pipeline, resolving a cost anomaly, or making a critical dataset trustworthy
• Create and validate a technical roadmap covering target architecture, technology choices, and migration path
• Improve data engineering standards for repository structure, Git workflow, CI/CD, environments, code review, and deployment
• Scale the data platform with data volume and product growth while controlling pipeline costs
• Reduce incidents and time-to-detect for critical datasets
• Implement observability with freshness, volume, and schema checks, alerting, SLAs, and ownership
• Enable new product analytics, in-product data, and ML/AI use cases
• Serve as a data architecture reference for the C-suite and Product