Spark Scala Developer

14 hours ago


Chennai Pune, India Cloudxtreme Full time US$ 1,20,000 - US$ 2,00,000 per year

Mandatory - Experience and knowledge in designing, implementing, and managing non-relational data stores (e.g., MongoDB, Cassandra, DynamoDB), focusing on flexible schema design, scalability, and performance optimization for handling large volumes of unstructured or semi-structured data.

Mainly client needs No SQL DB, either MongoDB or HBase

  • Data Pipeline Development: Design, develop, test, and deploy robust, high-performance, and scalable ETL/ELT data pipelines using Scala and Apache Spark to ingest, process, and transform large volumes of structured and unstructured data from diverse sources.
  • Big Data Expertise: Leverage expertise in the Hadoop ecosystem (HDFS, Hive, etc.) and distributed computing principles to build efficient and fault-tolerant data solutions.
  • Advanced SQL: Write complex, optimized SQL queries and stored procedures.
  • Performance Optimization: Continuously monitor, analyze, and optimize the performance of data pipelines and data stores. Troubleshoot complex data-related issues, identify bottlenecks, and implement solutions for improved efficiency and reliability.
  • Data Quality & Governance: Implement data quality checks, validation rules, and reconciliation processes to ensure the accuracy, completeness, and consistency of data. Contribute to data governance and security best practices.
  • Automation & CI/CD: Implement automation for data pipeline deployment, monitoring, and alerting using tools like Apache Airflow, Jenkins, or similar CI/CD platforms.
  • Documentation: Create and maintain comprehensive technical documentation for data architectures, pipelines, and processes.

Required Skills & Qualifications:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
  • Minimum 5 years of professional experience in Data Engineering, with a strong focus on big data technologies.
  • Proficiency in Scala for developing big data applications and transformations, especially with Apache Spark.
  • Expert-level proficiency in SQL; ability to write complex queries, optimize performance, and understand database internals.
  • Extensive hands-on experience with Apache Spark (Spark SQL, DataFrames, RDDs) for large-scale data processing and analytics.
  • Mandatory - Experience and knowledge in designing, implementing, and managing non-relational data stores (e.g., MongoDB, Cassandra, DynamoDB), focusing on flexible schema design, scalability, and performance optimization for handling large volumes of unstructured or semi-structured data.
  • Solid understanding of distributed computing concepts and experience with the Hadoop ecosystem (HDFS, Hive).
  • Experience with building and optimizing ETL/ELT processes and data warehousing concepts.
  • Strong understanding of data modeling techniques (e.g., Star Schema, Snowflake Schema).
  • Familiarity with version control systems (e.g., Git).
  • Excellent problem-solving, analytical, and communication skills.
  • Ability to work independently and collaboratively in an Agile team environment.

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