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Technical Lead

New Brunswick, NJ, United States

Job Title

DatabricksTechnical Lead

Relevant Experience

(inYrs)

10+

Must Have Technical/Functional Skills

Certification:Databricks certified Data Engineer Professional, SQL, Azure data Engineer,Python

PrimarySkillset:

Apache Spark: Strongunderstanding and hands-on experience with Apache Spark, including SparkSQL, Spark Streaming.

Databricks: Proficiency inusing Azure Databricks, including setting up and managing clusters,notebooks, and jobs.

Python and SQL: Strongprogramming skills in either Python and SQL as these are the primarylanguages used for writing Spark applications.

Data Processing and Analysis:Experience in designing and implementing data processing and analysispipelines using Spark and Databricks.

Distributed Computing:Knowledge of distributed computing concepts and experience withdistributed computing frameworks like Spark.

Cloud Platform: Familiaritywith Azure cloud platform and its services, including Azure DataFactory, Azure access, Azure Storage, Azure Data Lake, and Azure SQLDatabase.

Data Engineering: Understandingof data engineering concepts and experience with ETL processes, datamodeling, and data warehousing.

DevOps: Experience with CI/CDpipelines, version control systems (e.g., Git), and automated deploymentof Spark applications.

Performance Optimization:Ability to optimize Spark applications for performance and scalability,including tuning Spark configurations and leveraging Sparkoptimizations.

Data Security and Governance:Understanding of data security and governance practices, including dataencryption, access controls, and compliance regulations.

Monitoring and Troubleshooting:Proficiency in monitoring and troubleshooting Spark applications,identifying and resolving performance bottlenecks or issues.

Collaboration andCommunication: Excellent communication skills and the ability tocollaborate effectively with cross-functional teams, including datascientists, data engineers, and business stakeholders.

SecondarySkillset: Machine Learning: Familiaritywith machine learning concepts and experience with implementing machinelearning algorithms using Spark MLlib or other machine learningframeworks.

Data Visualization: Kn owledgeof data visualization tools and libraries like Matplotlib, Plotly, PowerBI or Tableau for presenting insights derived from data analysis.

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