Data Engineer – PySpark, Snowflake, AWS & GenAI

D4 Insight; Telangana; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

D4 Insight in Hyderabad, India seeks an experienced Data Engineer to design, build and maintain scalable data pipelines using PySpark, SQL, Python and Snowflake. You will implement Medallion Architecture across Bronze, Silver and Gold layers, and develop ingestion workflows with AWS Glue and Lambda.

The role requires hands-on Kafka-based streaming, Airflow orchestration, and a focus on data quality, performance and reliability, with exposure to GenAI tools such as LLMs and LangChain as an added

Qualifications

• 6–12 years of data engineering experience.
• Strong hands-on SQL, PySpark, Python and Snowflake.
• Experience building scalable data pipelines and reusable frameworks.
• Knowledge of Medallion Architecture and ETL/ELT processes.
• Hands-on with Kafka, AWS Glue, Lambda and workflow orchestration.

Responsibilities

• Design, develop, and maintain scalable data pipelines.
• Build reusable data engineering frameworks for efficient processing.
• Implement Medallion Architecture across Bronze, Silver, Gold layers.
• Develop ingestion/processing pipelines using AWS Glue and Lambda.
• Implement event-driven and streaming pipelines with Kafka.
• Orchestrate workflows using Airflow or equivalent tools.
• Optimize SQL, Snowflake workloads, and PySpark jobs.
• Ensure data quality, reliability and consistency.
• Explore GenAI data solutions using LLMs, RAG and LangChain.

Skills

SQL

PySpark

Python

Snowflake

Tools

Kafka

AWS Glue

AWS Lambda

Apache Airflow

Job description

Location: Hyderabad, India

Experience: 6–12 Years

Role Overview

We are looking for experienced Data Engineer with strong expertise in SQL, PySpark, Python, and Snowflake to design and maintain scalable data pipelines and engineering frameworks. The role involves cloud data services, Medallion Architecture, ETL/ELT workflows, and orchestration, with exposure to GenAI technologies as an added advantage.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using PySpark, SQL, Python, and Snowflake.
• Build reusable data engineering frameworks for efficient data processing and transformation.
• Implement Medallion Architecture across Bronze, Silver, and Gold layers.
• Develop data ingestion and processing pipelines using AWS Glue and AWS Lambda.
• Implement event-driven and streaming data pipelines using Kafka.
• Orchestrate workflows using Apache Airflow or equivalent tools.
• Optimize SQL queries, Snowflake workloads, and PySpark jobs for performance and scalability.
• Ensure data quality, reliability, and consistency across pipelines.
• Explore GenAI-based data solutions using LLMs, RAG, and LangChain.

Required Skills & Experience

• 6–12 years of relevant data engineering experience.
• Strong hands‑on expertise in SQL, PySpark, Python, and Snowflake.
• Experience building scalable data pipelines and reusable engineering frameworks.
• Strong understanding of Medallion Architecture and ETL/ELT processes.
• Hands‑on experience with Kafka, AWS Glue, and AWS Lambda.
• Experience with Apache Airflow or other workflow orchestration tools.
• Strong problem-solving skills and a focus on data quality and performance.

Good to Have: GCP, additional AWS services, MCP (Model Context Protocol), LLMs, RAG, and LangChain.