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PySpark Data Engineer

Rutherford, NJ, US • Posted 11 hours ago • Updated 11 hours ago

Contract W2

On-site

Fitment

Dice Job Match Score™

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Job Details

Skills

Banking
Brand
Financial Services
Computer Science
Information Technology
Streaming
API
Dimensional Modeling
SQL
Apache Hadoop
HDFS
Apache Hive
Cloud Computing
Amazon S3
Google Cloud Platform
Google Cloud
Storage
Electronic Health Record (EHR)
Databricks
Version Control
Git
Conflict Resolution
Problem Solving
Analytical Skill
Communication
Workflow
Orchestration
Apache Airflow
Microsoft Azure
Amazon Web Services
Step-Functions
Apache Kafka
Amazon Kinesis
NoSQL
Database
MongoDB
Apache Cassandra
Amazon DynamoDB
Continuous Integration
Continuous Delivery
Extract
Transform
Load
Data Warehouse
Apache Spark
Python
Data Quality
Management
Data Governance
Regulatory Compliance
Data Processing
Data Engineering
PySpark
Big Data

Summary

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

Required Skills & Experience

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 DataFrame 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, Google Cloud Platform) 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.

Desired 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 NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB).
Familiarity with data governance tools and practices.
Experience in a CI/CD environment.

What You Will Be Doing

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10105282
Position Id: 881610
Posted 11 hours ago

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