As a Data Engineering Manager at EY, you will lead the design and implementation of complex cloud analytics solutions using Databricks. You will collaborate with data and analytics professionals, management, and stakeholders to ensure that business requirements are translated into effective technical solutions.
The key responsibilities include:
• Understanding and analyzing business requirements to translate them into technical requirements.
• Designing, building, and operating scalable data architecture and modeling solutions.
• Staying up to date with the latest trends and emerging technologies to maintain a competitive edge.
You will also manage and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
To qualify for the role, you must have:
• Bachelor's degree in computer science, Engineering, or a related field required; Master's degree preferred.
• Typically, no less than 4 - 6 years relevant experience in data engineering, with a focus on cloud data solutions and analytics.
• Proven expertise in Databricks and experience with Spark for big data processing.
• Strong background in data architecture and design, with experience in building complex cloud analytics solutions.
• Experience in leading and managing teams, with a focus on mentoring and developing talent.
• Strong programming skills in languages such as Python, Scala, or SQL.
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication and interpersonal skills, with a focus on client management.
The ideal candidate will possess deep technical expertise in data architecture, cloud technologies, and analytics, along with exceptional leadership and client management skills.
This role offers the opportunity to work in a dynamic environment where you will face challenges that require innovative solutions. You will learn and grow as you guide others and interpret internal and external issues to recommend quality solutions.
Travel may be required regularly based on client needs.
Skills and attributes for success:
• Lead the design and development of scalable data engineering solutions using Databricks on cloud platforms (e.g., AWS, Azure, GCP).
• Oversee the architecture of complex cloud analytics solutions, ensuring alignment with business objectives and best practices.
• Manage and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement.
• Collaborate with clients to understand their analytics needs and deliver tailored solutions that drive business value.
• Ensure the quality, integrity, and security of data throughout the data lifecycle, implementing best practices in data governance.
• Drive end-to-end data pipeline development, including data ingestion, transformation, and storage, leveraging Databricks and other cloud services.
• Communicate effectively with stakeholders, including technical and non-technical audiences, to convey complex data concepts and project progress.
• Manage client relationships and expectations, ensuring high levels of satisfaction and engagement.
• Stay abreast of the latest trends and technologies in data engineering, cloud computing, and analytics.
• Strong analytical and problem-solving abilities.
• Excellent communication skills, with the ability to convey complex information clearly.
• Proven experience in managing and delivering projects effectively.
• Ability to build and manage relationships with clients and stakeholders.
Large-Scale Implementation Programs:
• Enterprise Data Lake Implementation: Led the design and deployment of a cloud-based data lake solution for a Fortune 500 retail client, integrating data from multiple sources (e.g., ERPs, POS systems, e-commerce platforms) to enable advanced analytics and reporting capabilities.
• Real-Time Analytics Platform: Managed the development of a real-time analytics platform using Databricks for a financial services organization, enabling real-time fraud detection and risk assessment through streaming data ingestion and processing.
• Data Warehouse Modernization: Oversaw the modernization of a legacy data warehouse to a cloud-native architecture for a healthcare provider, implementing ETL processes with Databricks and improving data accessibility for analytics and reporting.
Ideally, you'll also have:
• Experience with advanced data analytics tools and techniques.
• Familiarity with machine learning concepts and applications.
• Knowledge of industry trends and best practices in data engineering.
• Familiarity with cloud platforms (AWS, Azure, GCP) and their data services.
• Knowledge of data governance and compliance standards.
• Experience with machine learning frameworks and tools.
EY focuses on high-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.
