Scala Lead Engineer
1 day ago
Job Summary:
Skills: Lead Data Eng with Scala, Spark, Pyspark & Databricks
Job Title: Data Engineer (Scala, Spark, Pyspark, Databricks)
Location: Chennai
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 Databricks.
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