Roles and Responsibilities
- Architect and maintain enterprise-grade ELT and ETL data pipelines using Python, PySpark, Kafka, and Databricks to manage large-scale risk data.
- Build and deploy GenAI agents utilizing Google ADK, Google Flash 2.5+ LLMs, and Model Context Protocol (MCP) integrated with Human-in-the-Loop workflows.
- Design, automate, and deploy microservice integrations for data-intensive applications on OpenShift and Kubernetes using robust CI/CD pipelines.
- Implement data federation layers supporting Lambda and Data Mesh architectures via Starburst to enable AI/ML and NLP use cases.
- Leverage agentic AI platforms and development assistants such as Devin.AI and GitHub Copilot with prompt engineering to increase engineering velocity.
- Enforce data governance, risk management policies, and regulatory compliance standards across all data platforms.
Preferred Candidate Profile
- Work Experience: 8+ years in large-scale application development with 5+ years in a Python and PySpark Data Engineering lead role.
- Educational Background: Bachelor's degree in Computer Science, Engineering, or a related field (Master's degree preferred).
- Core Technical Skills: Python, PySpark, Databricks, Google ADK, LLMs, FastAPI, Spring Boot, Microservices, Kafka, SQL, Data Mesh, Starburst.
- Infrastructure and Cloud: Kubernetes, OpenShift, Docker, Cloud-Native Infrastructure, CI/CD pipelines.
- Industry Context: Data engineering experience in Banking Risk, Retail Products, Cards, Mortgage, Deposits, or Wealth Management.
- Assumed Requirements / Certifications: Databricks Certified Data Engineer, AWS Certified Data Analytics, or Azure Data Engineer Associate.