Job Description

Role : Databricks Architect

Location : Iselin NJ / New York, NY : Hybrid (2-3 Days) Local candidates only

Hire type : Contract (Preferred CTH)

Exp level : 15 years+


Immediate Joiner

Must have : Expert in Databricks , Pyspark , python, SQL

JD:

Strong knowledge on data bricks architecture and tools
Have experience of task and wf jobs creations in data bricks.
Deep understanding of distributed computing and how to use spark for data processing.
SQL and pyspark : strong command over querying databases and proficiency in pyspark.
Cloud platform: Preferred Azure for data bricks deployment.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent work experience
• 5+ years of experience in data engineering with Databricks and Spark
• Proficient in SQL and Python and Pyspark
• Experience with Azure Databricks Medallion Architecture with DLT, Iceberg
• Financial/Corporate Banking context would be a plus
• Experience with data integration and ETL tools, such as Azure Data Factory
• Experience with Azure cloud platform and services
• Experience with data warehouse and data lake concepts and architectures
• Good to have experience with big data technologies, such as Kafka, Hadoop, Hive, etc
• Strong analytical and problem-solving skills
• Excellent communication and teamwork skills

Responsibilities

• Design, develop, and maintain data pipelines using Databricks and Spark, and other cloud technologies as needed
• Optimize data pipelines for performance, scalability, and reliability
• Ensure data quality and integrity throughout the data lifecycle
• Collaborate with data scientists, analysts, and other stakeholders to understand and meet their data needs
• Troubleshoot and resolve data-related issues, and provide root cause analysis and recommendations
• Document data pipeline specifications, requirements, and enhancements, and communicate them effectively to the team and management
• Create new data validation methods and data analysis tools, and share best practices and learnings with the data engineering community
• Implement ETL processes and data warehouse solutions, and ensure compliance with data governance and security policies

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