Data Platform Engineer - Assistant Vice President

Overview

Hands-on senior data engineer in Finance Technology to build a scalable, high-quality data-intensive platform using Data Virtualization and Data Domains.

What you'll do

• Drive the creation of a scalable and high-quality data-intensive platform leveraging methodologies such as Data Virtualization and Data Domains.
• Apply data engineering principles across different data pipelines.
• Optimize and scale data processing and analytics solutions.

What you'll need

• 7+ years overall experience in Big Data or Enterprise large-scale application development using scalable tools like Databricks, Scala, Java, and the Python ecosystem.
• 5+ years of relevant experience implementing data-intensive solutions using agile methodologies.
• Strong expertise in Starburst/Trino with deep understanding of data federation and data virtualization architectures.
• Experience with real-time data processing and data virtualization technologies such as Starburst, Snowflake, and Flink.
• Deep understanding of data modeling, data warehousing concepts, methodologies, and best practices.
• Strong experience with data processing (e.g., Spark, Starburst, Snowflake, Redshift), storage (e.g., Hadoop, MongoDB, Oracle), and ETL (e.g., Airflow, Ab Initio, Talend) solutions.
• Experience with data engineering techniques such as building data lakes and data warehouses, data mesh architectures, data pipelines, and ETL vs. ELT patterns.
• Experience designing and developing distributed systems, handling structured and unstructured data to store, analyze, and report.
• Experience developing and maintaining large and complex database systems using relational databases like Oracle, and other big data technologies like Hadoop, Spark, etc.
• Proficient in PL/SQL and Unix Shell scripts.
• Strong SQL, PL/SQL development, and database tuning experience.
• Experience designing Online Transaction Processing (OLTP), Operational Data Store (ODS), and Data Warehouse applications.
• Experience with Data Quality and data lineage.
• Demonstrable experience with AI Agents, including their design, development, and integration into data engineering workflows and software systems.
• Practical knowledge of Model Context Protocol (MCP) servers, including setting up and configuring MCP servers to expose data tools, database connections, and APIs as contextual resources for LLM-powered agents.
• Experience designing MCP-compatible tool interfaces that allow agents to query, transform, and validate data autonomously.
• Understanding of MCP's client-server architecture and its role in enabling secure, standardized communication between AI agents and enterprise data systems.
• Understanding of Retrieval-Augmented Generation (RAG) principles, including vector databases and advanced retrieval techniques for grounding agents in enterprise data.
• Awareness of Large Language Models (LLMs) (e.g., OpenAI, Gemini, Claude, Llama) and their practical application in data engineering contexts such as automated schema inference, natural language querying, and intelligent data cataloging.
• Bachelor's degree/University degree or equivalent experience.

Nice to have

• Experience with real-time data processing and data virtualization technologies such as Starburst, Snowflake, and Flink.

Details

• Location: Pune, Maharashtra, India.
• Time type: Full time.