Job Description

Roles and Responsibilities

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Architect and maintain enterprise-grade ELT and ETL data pipelines using Python, PySpark, Kafka, and Databricks to manage large-scale risk data.

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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.

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Design, automate, and deploy microservice integrations for data-intensive applications on Open Shift and Kubernetes using robust CI/CD pipelines.

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Implement data federation layers supporting Lambda and Data Mesh architectures via Starburst to enable AI/ML and NLP use cases.

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Leverage agentic AI platforms and development assistants such as Devin.AI and Git Hub Copilot with prompt engineering to increase engineering velocity.

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Enforce data governance, risk management policies, and regulatory compliance standards across all data platforms.

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Preferred Candidate Profile

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Work Experience:



8+ years in large-scale application development with 5+ years in a Python and PySpark Data Engineering lead role.

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Educational Background: Bachelor's degree in Computer Science, Engineering, or a related field (Master's degree preferred).

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Core Technical Skills: Python, PySpark, Databricks, Google ADK, LLMs, FastAPI, Spring Boot, Microservices, Kafka, SQL, Data Mesh, Starburst.

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Infrastructure and Cloud: Kubernetes, Open Shift, Docker, Cloud-Native Infrastructure, CI/CD pipelines.

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Industry Context: Data engineering experience in Banking Risk, Retail Products, Cards, Mortgage, Deposits, or Wealth Management.

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Assumed Requirements / Certifications: Databricks Certified Data Engineer, AWS Certified Data Analytics, or Azure Data Engineer Associate.

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📌 Data Engineer Pune
🏢 Straive
📍 Pune