Databricks Data Engineer - Manager - Consulting

Position: Databricks Data Engineer - Manager - Consulting - Location Open 1

Databricks Data Engineer - Manager - Consulting - Location Open 1

EY

Location:

Anywhere in Country

At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and enable your career to go wherever you want it to. Join EY and help build a better working world.

Technology – Data and Decision Science – Data Engineering – Manager

We are looking for a dynamic, experienced Manager of Data Engineering to lead our team in designing and implementing complex cloud analytics solutions with a strong focus on Databricks. The ideal candidate possesses deep technical expertise in data architecture, cloud technologies, analytics, and exceptional leadership and client‑management skills.

Key Responsibilities

• 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.
• Leading workstream delivery and ensuring quality in all processes.
• Engaging with clients daily, participating in working sessions, and identifying opportunities for additional services.
• Implementing resource plans and budgets while managing engagement economics.

Skills and Attributes for Success

• Lead design and development of scalable data engineering solutions using Databricks on cloud platforms (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 quality, integrity, and security of data throughout the lifecycle, implementing governance best practices.
• Drive end‑to‑end data pipeline development, including ingestion, transformation, and storage, leveraging Databricks and other cloud services.
• Communicate effectively with stakeholders, conveying complex data concepts and project progress.
• Manage client relationships and expectations for high satisfaction.
• Stay abreast of the latest trends and technologies in data engineering, cloud computing, and analytics.
• Strong analytical and problem‑solving abilities.
• Excellent communication skills, conveying complex information clearly.
• Proven experience in managing and delivering projects effectively.
• Ability to build and manage relationships with clients and stakeholders.

Qualifications

• Bachelor’s degree in computer science, engineering, or related field required;
  Master’s preferred.
• 4–6 years of relevant experience in data engineering, focused on cloud data solutions and analytics.
• Proven expertise in Databricks and Spark for big data processing.
• Strong background in data architecture and design for complex cloud analytics solutions.
• Experience leading and managing teams, mentoring talent.
• Programming skills in Python, Scala, or SQL.
• Excellent problem‑solving skills, independent and team‑oriented.
• Strong communication and interpersonal skills focused on client management.

Required Expertise for Managerial Role

• Strategic Leadership:
  Align data engineering initiatives with organizational goals.
• Project Management:
  Manage multiple projects and teams, ensuring timely delivery.
• Stakeholder Engagement:
  Engage executives and stakeholders effectively.
• Change Management:
  Guide clients through data transformation and tech adoption.
• Risk Management:
  
  Identify risks and develop mitigation strategies.
• Technical Leadership:
  Lead technical discussions and architectural decisions.
• Documentation & Reporting:
  Create comprehensive documentation and reports.

Large-Scale Implementation Programs

• Enterprise Data Lake Implementation for a Fortune 500 retail client, integrating multiple data sources.
• Real‑Time Analytics Platform for a financial services organization, enabling fraud detection via streaming data.
• Data Warehouse Modernization for a healthcare provider, transitioning to a cloud‑native architecture.

Ideally, You’ll Also Have

• Advanced data…