Data Engineering

At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

Job Title: Junior Data Engineering Specialist

Experience Level: 3 - 5 Years

Designation: Supervising Associate

You will help build and maintain the data infrastructure underpinning the AI and analytics products within EY's Global Insights function. Our goal is to equip EY professionals with differentiated market, sector, and company insights to strengthen EY's brand, enhance client relationships, and drive commercial impact.

As part of the Applied AI and Analytics team, you will deliver scalable, trusted data infrastructure supporting a portfolio of automated products, self‑serve tools, and EY proprietary models. Working under the guidance of senior team members and in collaboration with analysts and business stakeholders, you will build and operate Azure-based data pipelines and databases, contribute to data warehouse and data lake solutions, help define technical requirements, and build and maintain BI and analytics dashboards. Your work ensures insights‑driven products are reliable, scalable, and easy to extend-supporting how EY people access and apply insights across the firm.

Key Responsibilities

• Responsible for the development, daily operation, and continuous improvement of data infrastructure for a set of analytics products, including ingestion, transformation, quality, and access.
• Co‑develop, test, and maintain Azure Data Factory pipelines to ingest, transform, and orchestrate data from multiple internal and external sources, supporting batch and near‑real‑time processing.
• Create relational and non‑relational databases, including Azure SQL Database and/or Azure Synapse SQL, and contribute to Azure data warehouse and data lake solutions.
• Write and optimize SQL queries and transformations, including stored procedures where appropriate, to support analytics products and downstream insight consumption.
• Assist in automating and scaling data flows using Git‑based workflows and deployment pipelines (e.g., Azure DevOps or GitHub), improving reliability, reuse, and performance.
• Perform daily monitoring of data pipelines and data quality, including validation checks, issue triage, and resolution.
• Maintain data documentation, metadata, and basic lineage tracking.
• Work with analysts and business stakeholders to help define technical requirements for new products.
• Follow established data security, privacy, and governance standards.
• Continue learning modern data engineering tools, platforms, and best practices.

Skills and Attributes for Success

• Demonstrated success in data engineering, analytics engineering, or a closely related technical role.
• Strong proficiency in SQL, including schema design, joins, aggregations, and query optimization.
• Experience working with relational databases (e.g., PostgreSQL, SQL Server, MySQL) and familiarity with non‑relational data stores.
• Experience with Microsoft Azure data services (e.g., Azure SQL, Data Factory, Data Lake), or strong exposure to cloud‑based data platforms.
• Understanding of data warehousing and data lake concepts, including batch and near‑real‑time data processing.
• Experience with ETL/ELT pipelines and data transformation workflows
• Working knowledge of Python or PySpark for data processing and transformation workflows is a plus
• Experience supporting data quality, pipeline monitoring, and issue resolution
• Familiarity with data security, privacy, and governance standards
• Proficiency with modern development workflows, including GitHub‑based collaboration and CI/CD pipelines.
• Experience building and supporting BI and analytics dashboards (e.g., Power BI), including working with DAX and semantic models.
• Effective communication with technical and non‑technical audiences, including translating business needs into technical tasks
• Demonstrated willingness to learn modern data engineering tools and best practices.

What We Look For

• 3 - 5 years of experience in data engineering, analytics, or a related field.
• Interest in building scalable, reliable data infrastructure and products
• A focus on delivering outcomes through continuous improvement
• Curiosity, collaborative mindset and eagerness to learn from teammates
• Comfort working in a fast-paced environment with evolving priorities

What We Offer

• Hands-on experience supporting enterprise data platforms and analytics use cases.
• Structured learning, mentorship, and skill development opportunities.
• Exposure to modern data technologies and best practices.
• A collaborative and inclusive environment focused on growth and impact.

EY | Building a better working world

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