Senior Specialist - Data Engineering

LTM

$100K — $130K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience with HDFS, Hive, and Spark
  • Hands-on expertise in the Hadoop ecosystem, particularly on-premise
  • Proficient in PySpark for large-scale data processing
  • Deep understanding of HDFS architecture and storage mechanisms
  • Experience in data ingestion using Sqoop
  • Strong knowledge of distributed data processing and big data concepts

Responsibilities

  • Design, develop, and maintain scalable data pipelines
  • Build and optimize Spark jobs for large volumes of data
  • Troubleshoot and resolve issues in data pipeline and processing
  • Ensure data quality governance and best practices
  • Work independently in fast-paced environments

Benefits

  • Exposure to cloud platforms (AWS, Azure, GCP) is a plus
  • Opportunity to work with large volumes of structured and unstructured data
  • Chance to deepen expertise in Hadoop ecosystem technologies
  • Engage in performance tuning and query optimization tasks
  • Collaborative work environment focused on problem-solving
Full Job Description
Role description

We are seeking a skilled Data Engineer with strong handson expertise in Hadoop ecosystem technologies particularly in onpremise environments The ideal candidate will have deep experience working with PySpark HDFS and Hive and a solid understanding of largescale data processing and distributed systems

Strong handson experience in Hadoop ecosystem onpremise

Expertise in PySpark for largescale data processing

Minimum 5 years of experience in HDFS Hive and Spark

Deep understanding of HDFS architecture and storage mechanisms

Proficiency in Hive for data warehousing and querying

Experience in building and optimizing Spark jobs

Handson experience with data ingestion using Sqoop

Strong knowledge of distributed data processing and big data concepts

Ability to design develop and maintain scalable data pipelines

Experience working with large volumes of structured and unstructured data

Strong skills in performance tuning and query optimization HiveSpark

Ability to troubleshoot and resolve data pipeline and processing issues

Good understanding of data quality governance and best practices

Exposure to cloud platforms AWSAzureGCP good to have

Strong problemsolving skills and ability to work independently in fastpaced environments

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