Data Engineer

Job Role 1: Data Engineer (ETL)

Job Role 2: Data Engineer (Scala)

Location: Hyderabad | Bangalore | Chennai | Noida | Pune
Work Mode: Hybrid
Working Hours: 11:00 AM 9:00 PM
Notice Period: Immediate to 15 Days

Email your CV: [email protected]

JD for Job Role 1: Data Engineer (ETL)

Experience Required: 7-8 Years

We are looking for a strong Data Engineer with hands-on experience in ETL/ELT processes, modern data engineering technologies, and AI/ML/LLM.

Key Skills & Requirements:

• Strong Data Engineering and ETL/ELT Processes

• Python

• Apache Spark PySpark / Spark SQL

• Azure Databricks Unity Catalog

• Snowflake

• Azure Functions

• Azure Service Bus

• SQL

• Terraform

• CI/CD Tools & Processes:

• GitHub Actions

• Git

• Artifactory

• Sonar

• AI/ML / LLM Experience Mandatory

JD for Job Role 2: Data Engineer (Scala)

Experience Required: 6+ Years

We are seeking a highly skilled and motivated Data Engineer with 6+ years of experience in designing, developing, and maintaining scalable data platforms and pipelines. The ideal candidate will possess strong expertise in Apache Spark, Scala, Python, SQL, and Cloud Technologies (Azure/AWS), with the ability to independently own and deliver end-to-end data engineering solutions. The role requires working closely with business stakeholders, data scientists, and engineering teams to build reliable, high-performance, and scalable data ecosystems that support analytics, reporting, and advanced data-driven initiatives.

Key Responsibilities:

• Design, develop, and maintain large-scale batch and real-time data pipelines using Apache Spark, Scala, and Python.
• Build scalable and reliable data processing frameworks for ingesting, transforming, and integrating data from multiple sources.
• Develop and optimize complex SQL queries, stored procedures, and data models to support reporting and analytics requirements.
• Design and implement cloud-based data solutions using Azure and/or AWS services.
• Create and manage ETL/ELT workflows using cloud-native tools such as Azure Data Factory, Azure Data Lake, AWS Glue, and Amazon S3.
• Collaborate with business stakeholders, data analysts, and data scientists to understand requirements and deliver data solutions.
  Monitor, troubleshoot, and optimize data pipelines to ensure performance, reliability, and data quality.
• Implement best practices for data governance, security, scalability, and operational excellence.
• Participate in code reviews, architecture discussions, and technical design sessions.
• Support CI/CD implementation and automation of data engineering workflows.
• Work with distributed data processing systems and contribute to platform modernization initiatives.
• Mentor junior team members and contribute to knowledge-sharing activities within the team.