EY-GDS Consulting-AI And DATA-Data Engineering-Manager

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. 

 

 

 

 

Data Engineering Manager / Data Architect

The Opportunity:
We are looking for a seasoned Data Engineering Manager with strong Data Architecture expertise to lead the design and delivery of scalable, enterprise-grade data solutions. This role combines technical leadership with strategic oversight, ensuring that data architecture aligns with business objectives and supports advanced analytics and AI initiatives. You will also serve as a key liaison with clients and stakeholders, translating business requirements into robust data architectures and actionable engineering plans.

 

Key Responsibilities:

• Lead and mentor a team of data engineers, fostering innovation, technical excellence, and collaboration.
• Define and drive the overall data architecture strategy, ensuring scalable, secure, and efficient data platforms that meet enterprise needs.
• Oversee the design and implementation of data ingestion frameworks, data models, and integration solutions that support complex analytics and AI workloads.
• Develop and manage CI/CD pipelines to enable rapid, reliable deployment of data products and solutions.
• Collaborate closely with clients and internal stakeholders to understand business requirements, provide technical guidance, and ensure alignment of data architecture with business goals.
• Act as a trusted advisor to clients, presenting architectural designs, solution roadmaps, and progress updates in a clear and compelling manner.
• Ensure adherence to data governance, data quality, and security standards across all data assets and pipelines.
• Drive the adoption of DevOps/DataOps principles within the team, promoting best practices in version control, automation, and monitoring.
• Lead Agile ceremonies and contribute to continuous improvement of team processes and delivery capabilities.
• Manage project priorities, resource allocation, and delivery timelines to meet client expectations and organizational objectives.

 

Technical Skills And Experience:

• Extensive experience in data architecture design and implementation, with a strong focus on scalable, cloud-based data platforms with 10-13 years of relevant experience.
• Proven leadership experience managing data engineering and architecture teams in complex enterprise environments.
• Deep expertise in AWS services including S3, EC2, Glue, Lambda, and Secrets Manager for building secure and scalable data solutions
• Hands-on experience with Databricks for unified data analytics and collaborative engineering.
• Proficiency with DBT (Data Build Tool) for data transformation, modeling, and pipeline orchestration.
• Skilled in Apache Airflow for workflow automation and pipeline management.
• Strong programming skills in Python, PySpark, Spark, and Scala for big data processing.
• Experience with software version control and CI/CD tools such as Git, Jenkins, and Apache Subversion.
• AWS certifications or equivalent professional technical certifications are highly desirable.
• Familiarity with cloud and on-premises middleware and enterprise integration technologies.
• Expertise in writing and optimizing Spark jobs and architecting data warehouse solutions.
• Excellent analytical skills with advanced SQL knowledge.
• Experience managing large-scale data warehousing environments and complex ETL/ELT pipelines.
• Strong understanding of data modeling concepts, data governance, and data quality frameworks.
• Exceptional communication skills, with proven ability to engage and influence clients and stakeholders at all levels.
• Experience in Python and/or Java development within data engineering and architecture contexts.
• Familiarity with Big Data ecosystems including EMR, Hadoop, Databricks, Hive, and Pyspark.

 

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