Data Engineer (Hadoop)

Position Overview

An experienced Senior Data Engineer with at least 5 years of hands-on experience in data engineering. The ideal candidate will have a solid understanding of big data technologies and be skilled in building scalable data infrastructure, designing ETL pipelines, and leveraging tools like Hadoop, PySpark, Kafka, and Apache NiFi.

Key Responsibilities

• Design, develop, and maintain large-scale, high-performance data systems and data pipelines using Python, PySpark, Hadoop, and Kafka.
• Build, deploy, and optimize ETL workflows to process and transform large volumes of structured and unstructured data.
• Collaborate with cross-functional teams to understand requirements and implement solutions that meet business needs.
• Work with Apache NiFi for data ingestion, transformation, and flow management.
• Write and optimize complex SQL queries for data manipulation and reporting.
• Apply strong data structures and algorithms knowledge to solve complex technical problems.
• Automate tasks and processes using Shell Script and Linux-based tools.
• Participate in code reviews, design discussions.
• Ensure adherence to best practices in software development, testing, and deployment.
• Continuously improve software performance, scalability, and reliability.
• Stay up-to-date with the latest developments in data engineering, big data technologies and incorporate them into the team's practices.

Required Skills and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• At least 5 years of professional software development experience, with strong expertise in the following:
• Python: Advanced proficiency in Python, including libraries like pandas, numpy, etc.
• PySpark: Experience with distributed data processing using PySpark.
• Hadoop: Familiarity with the Hadoop ecosystem, including HDFS, MapReduce, and related tools.
• Kafka: Hands-on experience in building and maintaining Kafka-based messaging systems.
• SQL: Strong knowledge of relational databases and advanced SQL querying.
• Data Structures & Algorithms: Strong understanding and practical application of data structures and algorithms.
• Data Engineering Best Practices: Deep understanding of data modeling, pipeline design, and data infrastructure architecture.
• ETL Pipelines: Expertise in designing, building, and maintaining efficient ETL pipelines.
• Apache NiFi: Knowledge of data flow management using Apache NiFi.
• Shell Scripting: Proficiency in writing efficient shell scripts for task automation.
• Linux: Strong knowledge of Linux systems and tools for development and deployment.
• Experience with Agile development methodologies.
• Excellent problem-solving skills and ability to troubleshoot complex technical issues.
• Strong communication skills with the ability to work in a collaborative team environment.

Preferred Qualifications

• Experience with platforms like Cloudera, Databricks.
• Familiarity with containerization technologies like Docker and Kubernetes.
• Knowledge of data warehousing and data lakes.