Data Pipeline Development
- Independently design, build, and maintain complex ETL pipelines, ensuring scalability and efficiency for large-scale data processing needs.
- Manage pipeline complexity and orchestration, delivering high-performance data products accessible via APIs for business-critical applications.
- Archive processed data products into data lakes (e.g., AWS S3) for analytics and machine learning use cases.
Anomaly Detection and Data Quality
- Implement advanced anomaly detection systems and data validation techniques, ensuring data integrity and quality.
- Leverage AI/ML methodologies, including Large Language Models (LLMs), to detect and address data inconsistencies.
- Develop and automate robust data quality and validation frameworks.
Cloud and API Engineering
- Architect and manage resilient APIs using modern patterns, including microservices, RESTful design, and GraphQL.
- Configure API gateways, circuit breakers, and fault-tolerant mechanisms for distributed systems.
- Ensure horizontal and vertical scaling strategies for API-driven data products.
- Monitoring and Observability
- Implement comprehensive monitoring and observability solutions using Prometheus and Grafana to optimize system reliability.
- Establish proactive alerting systems and ensure real-time system health visibility.
Cross-functional Collaboration and Innovation
- Collaborate with stakeholders to understand business needs and translate them into scalable, data-driven solutions.
- Continuously research and integrate emerging technologies to enhance data engineering practices.