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EY GDS – Data and Analytics - D and A – AWS Data Engineer - Senior

As part of our EY-GDS Data & Analytics (D&A) team, you will help clients solve complex business challenges using data-driven solutions. Our work spans across industries such as Banking, Insurance, Manufacturing, Healthcare, Retail, Supply Chain, Finance, and more. We help organizations unlock insights, modernize their data ecosystems, and enable scalable analytics platforms.

The Opportunity

We are looking for skilled AWS Data Engineers with strong hands-on experience in PySpark, SQL, ETL pipelines, AWS services, and modern data lakehouse/lake architectures. This is an excellent opportunity to join a fast-growing D&A practice within EY, working on high-impact, enterprise-scale data projects.

Key Responsibilities

• Develop, optimize, and deploy scalable ETL/ELT pipelines using PySpark, SQL, and AWS services.
• Build and manage data lakehouse solutions leveraging AWS S3, Glue, Iceberg, and other AWS-native components.
• Migrate on premises ETL workloads to modern AWS-based architectures with a focus on performance, reliability, and cost efficiency.
• Implement metadata-driven ingestion frameworks and medallion/layered architecture (Bronze/Silver/Gold).
• Work on orchestration frameworks such as Astronomer (Airflow), AWS Step Functions, or AWS-managed workflows.
• Design and optimize Spark-based data processing jobs for high throughput.
• Follow data warehouse (DW) concepts and best practices for modeling and data integration.
• Collaborate with data analysts, data scientists, and BI engineers for seamless data delivery.
• Perform code reviews, troubleshoot issues, and ensure end-to-end data quality.
• (Optional) Leverage Databricks for PySpark, Delta Lake, Lakehouse, or workflow orchestration where applicable.

Skills and Attributes for Success

• 4+ years of total IT experience with at least 2+ years in AWS-based data engineering.
• Strong hands-on experience in:
  • PySpark, SQL, Python
  • ETL pipeline development
  • AWS services: S3, Glue, Lambda, Step Functions, CloudWatch
  • Unix/Linux environments
  • Iceberg table format
  • Astronomer (Airflow) or other orchestration tools
• Experience with structured and semi structured data formats such as Parquet, JSON, CSV, XML.
• Understanding of data warehousing concepts, star/snowflake schemas, and dimensional modeling.
• Good knowledge of CI/CD, version control (GitHub, Azure DevOps, Jenkins).
• Strong analytical, troubleshooting, and problem solving skills.
• Ability to work independently and collaborate with stakeholders to deliver high quality solutions.
• (Optional but good to have) Experience working with Databricks, Delta Lake, or Unity Catalog.

To Qualify, You Must Have

• Bachelor’s or Master’s degree in Computer Science, IT, or related field.
• 4–7 years of industry experience in data engineering.
• Production-grade experience building and managing AWS data pipelines.
• Hands-on experience with Agile/Scrum delivery models.
• Strong communication and stakeholder management skills.
• Proactive, self driven approach with ownership of deliverables.

Ideally, You'll Also Have

• Client-facing and stakeholder management experience.
• Experience working in large-scale, multi-environment data platforms.

What We Look For

• Technically strong, curious, and adaptable professionals who enjoy solving challenging data problems and continuously learning new technologies in a fast-moving environment.

What Working at EY Offers

• Opportunities to work on diverse, meaningful, and industry-leading projects.
• Coaching, learning programs, and a personalized growth and development plan.
• Exposure to a collaborative, interdisciplinary work culture.
• Flexibility to manage your work in the way that suits you best.
• Supportive colleagues and a global environment for continuous knowledge exchange.

EY | Building a better working world

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