Data Engineer (SQL, Python, Spark, Hive, Hadoop)

Role Overview

We are looking for a skilled Data Engineer with 5 years of hands-on experience in designing, developing, and optimizing big data pipelines and solutions. The ideal candidate will have strong expertise in SQL, Python, Apache Spark, Hive, and Hadoop ecosystems and will be responsible for building scalable data platforms to support business intelligence, analytics, and machine learning use cases.

Key Responsibilities

• Design, develop, and maintain scalable ETL pipelines using Spark, Hive, and Hadoop.
• Write efficient SQL queries for data extraction, transformation, and analysis.
• Develop automation scripts and data processing workflows using Python.
• Optimize data pipelines for performance, reliability, and scalability.
• Work with structured and unstructured data from multiple sources.
• Ensure data quality, governance, and security throughout the data lifecycle.
• Collaborate with cross-functional teams (Data Scientists, Analysts, and Business stakeholders) to deliver data-driven solutions.
• Monitor and troubleshoot production data pipelines.

Required Skills & Qualifications

• 5+ years of experience in Data Engineering / Big Data development.
• Strong expertise in SQL (query optimization, performance tuning, stored procedures).
• Proficiency in Python for data manipulation, scripting, and automation.
• Hands-on experience with Apache Spark (PySpark/Scala) for large-scale data processing.
• Solid knowledge of Hive for querying and managing data in Hadoop environments.
• Strong working knowledge of Hadoop ecosystem (HDFS, YARN, MapReduce, etc.).
• Experience with data pipeline orchestration tools (Airflow, Oozie, or similar) is a plus.
• Familiarity with cloud platforms (AWS, Azure, or GCP) is preferred.
• Excellent problem-solving, debugging, and communication skills.