1. Design, build, and maintain scalable data pipelines for structured and unstructured financial datasets.

2. Work with real-time and batch data processing systems for investment research workflows.

3. Develop ETL/ELT pipelines using modern cloud-native technologies.

4. Integrate market, financial, and alternative data sources into centralized data platforms.

5. Optimize data architecture for performance, reliability, and scalability.

6. Collaborate with AI/ML teams to support agentic workflows and intelligent automation.

7. Build and maintain data models for analytics, reporting, and research use cases.

8. Ensure data quality, governance, security, and compliance standards are maintained.

9. Support scaling of platform components from MVP/v0.1 to stable production-ready systems.

10. Participate in architecture discussions and contribute to system design decisions.

Required Skills:

5+ years of experience in Data Engineering.

1. Strong experience with Python, SQL, and data processing frameworks.

2. Hands-on experience with cloud platforms such as Microsoft Azure, AWS, or GCP.

3. Expertise in ETL/ELT pipeline development.

4. Experience with Databricks, Spark, Kafka, or similar distributed data technologies.

5. Strong understanding of data warehousing and lakehouse architectures.

6. Experience working with APIs, streaming data, and large-scale datasets.

7. Knowledge of CI/CD and DevOps practices for data platforms.

8. Understanding of data governance and security best practices.