Hadoop Developer

Cloudxtreme; Pune District; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Cloudxtreme in Pune, India, seeks a senior data engineering expert to design and optimize large-scale Hadoop/Hive data platforms. You will shape data models, implement HiveQL, and lead initiatives across data lakes.

The role requires deep PySpark skills, experience with Hadoop components (HDFS, YARN), and familiarity with Parquet/ORC formats; strong communication to explain complex concepts to non-technical stakeholders.

Join a technically driven team delivering scalable analytics solutions.

Qualifications

• Bachelor's degree in CS/IT or related field; master's preferred.
• 8-10+ years hands-on data modeling, database design, and data architecture.
• Expert HiveQL and deep understanding of Hive architecture.
• Strong PySpark experience for data processing, transformation, and optimization.
• Experience with Hadoop ecosystem and big data technologies such as Kafka, Spark Streaming, HBase, Impala, or Presto.

Skills

HiveQL

PySpark

Data modeling

Big data concepts

Leadership

Communication

Problem solving

Education

Bachelor's degree in CS/IT or related field

Master's degree preferred

Tools

Apache Hadoop

HDFS

YARN

Hive

Kafka

Spark

Presto

HBase

Parquet/ORC/Avro

Job description

Role & responsibilities

Qualifications

• Bachelor's degree in computer science, Information Technology, or a related quantitative field. Master's degree preferred.
• 8-10+ years of hands-on experiencein data modeling, database design, and data architecture, with aprimary focus on Hadoop/Hive ecosystems and big data technologies.
• Expert-levelproficiencyin HiveQLand deep understanding of Hivearchitecture.
• Strong hands-on experience with Apache Hadoop(HDFS, YARN) and related components.
• MandatoryproficiencyinPySparkfor data processing, transformation, and optimization.
• Demonstrable experience in designing and implementing dimensional models, 3NF, Data Vault, or other relevant data modeling techniques for large-scale data warehouses/lakes.
• Experience with other big data technologies such as Kafka, Spark Streaming, HBase, Impala, or Presto is highly desirable.
• Strong understanding of data partitioning, bucketing, and file formats (Parquet, ORC, Avro) within Hadoop for performance optimization.
  Familiarity with cloud-based big data platforms (e.g., AWS EMR, Azure HDInsight, Google CloudDataproc) is a significant plus.
• Excellent analytical, problem-solving, and communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
• Proven ability to lead technical initiatives, manage complex projects, and drive architectural decisions.