Job Description

Role: Senior Data Engineer

Location: Reading, PA (onsite-Hybrid)

Term: Contract

The Team

• You will be part of a high-performing Data Engineering & Analytics team responsible for building scalable, cloud-native data platforms on AWS. The team partners closely with product, analytics, and business stakeholders to deliver reliable data pipelines that support advanced analytics, reporting, and data-driven decision-making.
• This team emphasizes modern data engineering practices , reusable frameworks, performance optimization, and production-grade data solutions.

As a Senior Data Engineer , you will design, build, and maintain end-to-end data pipelines leveraging Talend Cloud (8.0) , PySpark , and AWS services . You will play a key role in ingesting, transforming, and optimizing large-scale structured and unstructured datasets while ensuring scalability, performance, and data quality across the platform.

Key responsibilities include:

• Designing and developing ETL/ELT workflows using Talend 8.0 on Cloud
• Integrating data from APIs, flat files, and streaming sources
• Ingesting and managing data in AWS S3–based data lakes
• Developing PySpark jobs for large-scale data processing and transformations
• Implementing Spark SQL for complex transformations and schema management
• Building and supporting cloud-native data pipelines using AWS services such as Glue, Lambda, Athena, and EMR
• Applying performance tuning and optimization techniques for Spark workloads

What You Will Bring

• 10+ years of experience in data engineering, big data, or ETL development roles
• Strong hands-on expertise with Talend ETL (Talend Cloud / Talend 8.x)
• Advanced experience in PySpark and Spark SQL for large-scale data processing
• Proficiency in Python , including building reusable data transformation modules
• Solid experience with AWS data services , including:
• S3 for data lake storage and lifecycle management
• Glue for ETL/ELT orchestration
• Lambda for event-driven processing
• Athena for serverless analytics
• EMR for Spark/PySpark workloads
• Strong understanding of ETL/ELT patterns, data lakes, and distributed systems
• Ability to optimize performance, ensure data quality, and build production-ready pipelines
• Excellent collaboration skills and experience working with cross-functional teams

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