Senior Talend Lead

Our Company

We’re Hitachi Digital Services, a global digital solutions and transformation business with a bold vision of our world’s potential. We’re people-centric and here to power good. Every day, we future-proof urban spaces, conserve natural resources, protect rainforests, and save lives. This is a world where innovation, technology, and deep expertise come together to take our company and customers from what’s now to what’s next. We make it happen through the power of acceleration.

Imagine the sheer breadth of talent it takes to bring a better tomorrow closer to today. We don’t expect you to ‘fit’ every requirement – your life experience, character, perspective, and passion for achieving great things in the world are equally as important to us.

Location: Reading, PA (Hybrid)

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.

 

The Role

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

About us

We’re a global, team of innovators. Together, we harness engineering excellence and passion to co-create meaningful solutions to complex challenges. We turn organizations into data-driven leaders that can make a positive impact on their industries and society. If you believe that innovation can bring a better tomorrow closer to today, this is the place for you.

#LI-RS2

 

Fostering innovation through diverse perspectives

Hitachi is a global company operating across a wide range of industries and regions. One of the things that sets Hitachi apart is the diversity of our business and people, which drives our innovation and growth.

We are committed to building an inclusive culture based on mutual respect and merit-based systems. We believe that when people feel valued, heard, and safe to express themselves, they do their best work.

How we look after you

We help take care of your today and tomorrow with industry-leading benefits, support, and services that look after your holistic health and wellbeing. We’r