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