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Data Engineer - Scala / Apache Spark

Data Engineer - Scala / Apache Spark

Ixceed SolutionsHyderabad
2 days ago
Job description

Responsibilities :

  • Design, develop, and maintain robust and scalable data pipelines using Apache Spark and Scala on the Databricks platform.
  • Implement ETL (Extract, Transform, Load) processes for various data sources, ensuring data quality, integrity, and efficiency.
  • Optimize Spark applications for performance and cost-efficiency within the Databricks environment.
  • Work with Delta Lake for building reliable data lakes and data warehouses, ensuring ACID transactions and data versioning.
  • Collaborate with data scientists, analysts, and other engineering teams to understand data requirements and deliver solutions.
  • Implement data governance and security best practices within Databricks.
  • Troubleshoot and resolve data-related issues, ensuring data availability and reliability.
  • Stay updated with the latest advancements in Spark, Scala, Databricks, and related big data technologies.

Required Skills and Experience :

  • Proven experience as a Data Engineer with a strong focus on big data technologies.
  • Expertise in Scala programming language for data processing and Spark application development.
  • In-depth knowledge and hands-on experience with Apache Spark, including Spark SQL, Spark Streaming, and Spark Core.
  • Proficiency in using Databricks platform features, including notebooks, jobs, workflows, and Unity Catalog.
  • Experience with Delta Lake and its capabilities for building data lakes.
  • Strong understanding of data warehousing concepts, data modeling, and relational databases.
  • Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their data services.
  • Experience with version control systems like Git.
  • Excellent problem-solving and analytical skills.
  • Ability to work independently and as part of a team.

    • Preferred Qualifications (Optional) :

  • Experience with other big data technologies like Kafka, Flink, or Hadoop ecosystem components.
  • Knowledge of data visualization tools.
  • Understanding of DevOps principles and CI / CD pipelines for data engineering.
  • Relevant certifications in Spark or Databrick

    • (ref : hirist.tech)

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