Senior Data Engineer – Scalable Pipelines & Cloud Analytics

Prudential Financial; Ballyboe Glencar; On-site; EUR 90,000 - 130,000; Full time

Job summary

Prudential Financial is seeking a Senior Data Engineer to partner with architects, engineers and data scientists to build scalable data pipelines and platforms. You will design, develop, test and maintain data solutions to support analytics and decision making.

Strong coding, collaboration and a continuous learning mindset are essential. You will leverage Python, SQL, Spark and AWS services to deliver reliable data assets, while applying DevOps practices and best practices across the data

Qualifications

• Bachelor's degree in Computer Science or Engineering.
• Experience with DevOps automation tools and SDLC practices.
• Ability to learn and adapt in a collaborative environment.
• Strong problem solving and communication skills.
• Experience with data pipeline, data lake and data warehousing concepts.

Responsibilities

• Build and optimize data pipelines, storage systems and data quality checks.
• Develop high quality, well-documented code aligned with standards.
• Collaborate with data analysts, scientists and architects on data projects.
• Write tests (unit/integration) and automate quality checks.
• Apply knowledge of AWS, Spark, and data warehousing to deliver solutions.

Skills

Python

R

SQL

Java

Scala

Pyspark

Shell scripting

DevOps

SDLC

Communication

Teamwork

Education

Bachelor of Computer Science or Engineering

Tools

AWS

Kinesis

Kafka

Airflow

Tableau

Power BI

Job description

Prudential Financial is seeking a Senior Data Engineer to partner with architects, engineers and data scientists to build scalable data pipelines and platforms. You will design, develop, test and maintain data solutions to support analytics and decision making.

Strong coding, collaboration and a continuous learning mindset are essential. You will leverage Python, SQL, Spark and AWS services to deliver reliable data assets, while applying DevOps practices and best practices across the data