PySpark Data Engineer

Grow your career as an PySpark Data Engineer with an innovative global bank in Rutherford, NJ. Contract role with strong possibility of extension. Will require working a hybrid schedule 3 days onsite per week.

Join one of the world's most renowned global banks and trusted brand with over 200 years of continuously evolving financial services worldwide. You will work alongside some of the smartest minds in the industry who are excited to share their knowledge and to learn from you.

Contract Duration: 12 Months

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related quantitative field.
• 7-10 years of experience as a Data Engineer, with significant experience specifically in PySpark.
• Strong proficiency in Python programming.
• Extensive experience with Apache Spark, including Spark SQL, Spark Streaming, and Data Frame API.
• Solid understanding of data warehousing concepts, dimensional modeling, and ETL principles.
• Proficiency in SQL for data querying and manipulation.
• Experience with big data technologies such as Hadoop, HDFS, Hive, or similar.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their data services (e.g., S3, ADLS, Google Cloud Storage, EMR, Databricks, Glue).
• Experience with version control systems (e.g., Git).
• Excellent problem-solving, analytical, and communication skills.

• Master's degree in a related field.
• Experience with workflow orchestration tools (e.g., Apache Airflow, Azure Data Factory, AWS Step Functions).
• Knowledge of stream processing technologies (e.g., Kafka, Kinesis).
• Experience with No
  
  SQL databases (e.g., MongoDB, Cassandra, DynamoDB).
• Familiarity with data governance tools and practices.
• Experience in a CI/CD environment.

• Design, build, and optimize data pipelines using PySpark to extract, transform, and load (ETL) data from various sources into data lakes and data warehouses.
• Develop and maintain scalable data processing jobs and frameworks using Apache Spark with Python (PySpark).
• Work closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver high-quality data solutions.
• Implement data quality checks, monitoring, and alerting for data pipelines to ensure data accuracy and reliability.
• Optimize existing PySpark jobs for performance, efficiency, and cost-effectiveness.
• Manage and process large datasets, ensuring data governance, security, and compliance.
• Troubleshoot and resolve issues in data pipelines and data processing jobs.
• Participate in code reviews, contribute to architectural discussions, and promote best practices in data engineering.
• Stay informed about new PySpark features, big data technologies, and industry best practices.
• Document data pipelines, data models, and processes.