Senior Data Engineer

Tiger Analytics Inc.•Jersey City, NJ

About The Position

Tiger Analytics is seeking a Senior Azure Data Engineer to design, develop, and maintain scalable data pipelines and ETL/ELT solutions for enterprise data platforms. The role uses Microsoft Azure data services, Python, Scala, and Apache Spark, and supports reliable data ingestion, transformation, processing, and delivery across diverse data sources. The engineer will work with Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Spark, and Hadoop-related technologies. The role also includes data quality controls, production support, performance tuning, deployment practices, and collaboration with data architects, analysts, and cross-functional teams.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Computer Engineering, or a related field.
10+ years of overall Data Engineering experience.
Strong hands-on experience with Microsoft Azure data services, particularly Azure Data Factory, Azure Databricks, and Azure Data Lake Storage.
Proficiency in Python and Scala for data processing and application development.
Strong experience with Apache Spark and distributed data processing.
Hands-on experience with Hadoop and big data technologies.
Strong understanding of ETL/ELT processes, data transformations, and data pipeline architecture.
Strong SQL skills and experience with performance tuning, debugging, troubleshooting, and production support.
Experience with Control-M for batch scheduling, job monitoring, workflow orchestration, and dependency management.
Familiarity with version control, CI/CD, and deployment practices.
Excellent analytical, problem-solving, and collaboration skills.

Responsibilities

Design, develop, and maintain scalable data pipelines using Azure data services, Python, Scala, and Apache Spark.
Develop and optimize ETL/ELT processes for data ingestion, transformation, and processing across diverse data sources.
Build and manage data pipelines using Azure Data Factory, Azure Databricks, and Azure Data Lake Storage.
Develop and optimize Spark applications to improve data processing performance, scalability, and reliability.
Work with Hadoop and related big data technologies where applicable.
Monitor pipeline execution, troubleshoot failures, and resolve data processing issues to ensure reliable data delivery.
Implement data validation, error handling, and data quality checks to ensure accuracy and consistency.
Collaborate with data architects, analysts, and cross-functional teams to understand requirements and deliver reliable data solutions.
Follow coding standards, testing practices, version control, and CI/CD procedures.
Support production deployments, incident resolution, performance tuning, and ongoing maintenance of data engineering solutions.