Senior Data Engineer (Databricks Lead)

Prodapt; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Prodapt seeks a Senior Data Engineer (Databricks Lead) with 12-16 years of experience to architect and optimize enterprise-scale data platforms. You will drive real-time and batch data pipelines using Databricks, Spark, Snowflake, Kafka, Python, SQL, and AWS, enabling analytics, AI/ML, and Generative AI initiatives.

You will provide technical leadership, mentor teams, and collaborate with Analytics, Data Science, and AI groups to deliver scalable, compliant data solutions.

Qualifications

• 12-16 years of experience in Data Engineering and Data Platform Architecture.
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or related field.
• Proven experience leading enterprise-scale data modernization and cloud migration initiatives.

Responsibilities

• Lead architecture, design, and implementation of scalable data platforms using Databricks, Snowflake, and AWS.
• Design and optimize large-scale batch and real-time data pipelines with Spark and Kafka.
• Develop and maintain robust ETL/ELT frameworks for reliability and performance.

Skills

Databricks

Apache Spark

Snowflake

Kafka

Python

SQL

AWS

Education

Bachelor's or Master's degree in CS/IT/Engineering

Job description

Overview

We are looking for a Senior Data Engineer (Databricks Lead) with 12-16 years of experience to lead the design, development, and optimization of enterprise-scale data platforms. The ideal candidate will have deep expertise in Databricks, Apache Spark, Snowflake, Kafka, Python, SQL, and AWS, along with experience building scalable data architectures that support analytics, real-time processing, AI/ML, and Generative AI initiatives.

Responsibilities

• Lead the architecture, design, and implementation of scalable, secure, and high-performance data platforms using Databricks, Snowflake, and AWS.
• Design and optimize large-scale batch and real-time data pipelines using Spark and Kafka.
• Develop and maintain robust ETL/ELT frameworks to ensure data reliability, scalability, and performance.
• Implement real-time data ingestion and processing solutions using Kafka and Spark Streaming.
• Build automated data integration frameworks leveraging REST APIs, GraphQL APIs, and Databricks APIs.
• Design cloud-native data solutions aligned with enterprise governance, security, and compliance standards.
• Develop data models using Star Schema, Snowflake Schema, Kimball, and Inmon methodologies.
• Provide technical leadership, architecture guidance, and mentorship to engineering teams.
• Collaborate with Analytics, Data Science, and AI teams to enable Machine Learning, Generative AI, and LLM-based solutions.
• Troubleshoot and resolve platform performance bottlenecks and scalability challenges.
• Drive engineering best practices, code quality standards, and data governance initiatives.

Requirements

Technical Skills

• Strong expertise in Databricks and Apache Spark.
• Hands-on experience with Snowflake and modern data warehouse architectures.
• Proficiency in Kafka and real-time stream processing.
• Strong programming skills in Python and advanced SQL.
• Experience with AWS Cloud Services.
• Expertise in designing and implementing ETL/ELT architectures.
• Strong understanding of data modeling concepts and methodologies.
• Experience integrating systems using REST and GraphQL APIs.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• 12-16 years of experience in Data Engineering and Data Platform Architecture.
• Proven experience leading enterprise-scale data modernization and cloud migration initiatives.

Preferred Skills

• Experience with AI/ML data platforms, Generative AI, and LLM enablement.
• Exposure to Data Governance and Data Quality frameworks.
• Strong stakeholder management, communication, and technical leadership skills.