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# Azure Data Engineer

- **Company:** Infinite Computer Solutions (ICS)
- **Location:** Berkeley Heights, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Continuous Integration, Information Engineering, Data Governance, Extract Transform Load (ETL), Apache Hive, SQL Azure, Performance Tuning, Scrum Methodology, Power BI, Cloud Services, Azure Data Lake, Software Deployment, Azure Data Factory, Git, Data Lakes, Pyspark, Star Schema, Data Pipelines, Databricks
- **Published:** October 14, 2026
- **Apply:** https://www.dice.com/job-detail/7be303ae-cacf-4f85-9f6f-958f7f60be41

## About the Role

* 8+ years of experience in Data Engineering.
 * Mandatory Banking/Financial Services experience (ALM, Risk, Treasury, Regulatory, Finance, or Capital Markets preferred).
 * Strong hands-on experience with Azure Databricks, PySpark, Spark SQL, Azure Data Factory (ADF), Azure SQL, and ADLS Gen2.
 * Experience designing and developing ETL/ELT pipelines and Lakehouse (Bronze/Silver/Gold) architecture.
 * Strong SQL/T-SQL development and data modeling (Star/Snowflake Schema).
 * Experience with Delta Lake, incremental loads, CDC, and data quality frameworks.
 * Knowledge of Power BI and semantic models is a plus.
 * Experience with Azure DevOps, Git, and CI/CD.
 * Excellent problem-solving, communication, and Agile/Scrum experience.

## Description

We are hiring a Senior Data Engineer with 8+ years of experience in Data Engineering and mandatory Banking/Financial Services (BFSI) domain experience. The ideal candidate will have strong expertise in building cloud-native data platforms on Microsoft Azure using Azure Databricks, Azure Data Factory, and Azure Data Lake., * Design, develop, and maintain scalable Azure data pipelines using ADF and Databricks.

 * Build and optimize PySpark/Spark SQL transformations for enterprise data processing.
 * Develop Delta Lake/Lakehouse solutions and curate analytical datasets.
 * Implement data quality, reconciliation, and performance optimization.
 * Collaborate with business, risk, finance, and data stakeholders to deliver scalable data solutions.
 * Support production deployments, troubleshooting, and CI/CD automation.