01Overview
ROLES and RESPONSIBILITIES
- Design, build, and maintain end-to-end data pipelines from ingestion to curated, trusted data models.
- Develop scalable and maintainable data transformation layers using modern transformation frameworks.
- Design and optimize data warehouse schemas and data models to support reporting and analytics.
- Write highly effective, production-grade SQL and optimize complex queries for performance.
- Build, maintain, and monitor orchestration workflows, ensuring proper dependency management and data lineage.
- Implement data quality, validation, monitoring, and reliability practices to ensure trusted datasets.
- Make architecture and design decisions while balancing scalability, maintainability, and delivery speed.
- Collaborate closely with analysts, engineers, and business stakeholders to understand requirements and translate them into robust data solutions.
- Proactively identify improvements in data architecture, pipeline performance, and engineering practices.
- Leverage Large Language Models (LLMs) and AI-assisted development tools to improve engineering productivity and accelerate delivery.
- Build solutions that are production-ready, scalable, and resilient while maintaining high development velocity.
Technical Skills
- Expert-level proficiency in SQL, including complex query optimization, performance tuning, and advanced data modeling.
- Strong expertise in data warehousing concepts, dimensional modeling, and analytical database design.
- Hands-on experience with modern data transformation frameworks such as:
- dbt
- SQLMesh
- Strong understanding of workflow orchestration, DAG design, dependency management, and data lineage.
- Experience designing and maintaining reliable, production-grade data pipelines.
- Strong knowledge of data quality frameworks, testing, validation, and monitoring.
- Excellent systems thinking with the ability to design scalable and maintainable data architectures.
- Proven ability to deliver high-quality solutions in fast-paced, agile environments. .
