AID&E - Databricks Data Engineer - Manager - Tech Cons - Open Location

EY•Hartford, DC

22h•$125,500 - $261,600

About The Position

EY is hiring a Data Engineering Manager to lead the design and implementation of complex cloud analytics solutions, with a strong focus on Databricks. The role involves translating business requirements into technical solutions, building and operating scalable data architectures and models, and keeping current with emerging technologies. The manager collaborates with data and analytics professionals, clients, management, and other stakeholders to deliver business value. The position leads technical workstreams and complex initiatives, oversees solution quality, manages and mentors data engineers, and maintains client relationships. Responsibilities also include managing resource plans, budgets, and engagement economics; guiding end-to-end data pipeline development; and ensuring data quality, integrity, security, and governance. Regular travel may be required based on client needs.

Requirements

Bachelor’s degree in computer science, engineering, or a related field.
Typically at least 4–6 years of relevant experience in data engineering, focused on cloud data solutions and analytics.
Proven expertise in Databricks and experience with Spark for big data processing.
Hands-on experience with AI-native data engineering practices, including generative AI and agentic workflows for accelerating pipeline development, testing, documentation, and optimization.
Strong background in data architecture and design, including building complex cloud analytics solutions.
Experience leading and managing teams, mentoring and developing talent.
Strong programming skills in Python, Scala, or SQL.
Familiarity with AI-powered IDEs and coding assistants such as Cursor, GitHub Copilot, or Databricks Assistant, and their use in day-to-day data engineering development.
Experience managing multiple projects and teams and ensuring delivery within project scope.
Ability to align data engineering initiatives with organizational goals and drive strategic vision.
Proficiency in stakeholder engagement, including understanding needs and presenting solutions to executives and other stakeholders.
Skills in guiding clients through change related to data transformation and technology adoption.
Ability to identify project risks and develop mitigation strategies.
Experience leading technical discussions and making architectural decisions.
Proficiency in creating documentation and reports to communicate project progress and outcomes to clients.
Strong analytical, problem-solving, communication, and interpersonal skills.
Ability to work independently and collaboratively and manage client relationships.
Master’s degree is preferred.

Nice To Haves

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 AWS, Azure, or GCP and their data services.
Knowledge of data governance and compliance standards.
Experience with machine learning frameworks and tools.

Responsibilities

Analyze business requirements and translate them into technical requirements and solutions.
Design, build, and operate scalable data architectures and data modeling solutions.
Lead the design and development of scalable Databricks data engineering solutions on cloud platforms such as AWS, Azure, or GCP.
Oversee the architecture of complex cloud analytics solutions, ensuring alignment with business objectives and best practices.
Lead workstream delivery and ensure quality across processes.
Manage and mentor data engineers, fostering innovation, collaboration, and continuous improvement.
Engage with clients in working sessions, understand their analytics needs, deliver tailored solutions, and identify opportunities for additional services.
Manage client relationships and expectations and maintain stakeholder satisfaction.
Drive end-to-end data pipeline development, including ingestion, transformation, and storage.
Ensure data quality, integrity, security, and governance throughout the data lifecycle.
Implement resource plans and budgets and manage engagement economics.
Manage multiple projects and teams, including delivery timelines and project scope.
Align data engineering initiatives with organizational goals and provide strategic leadership.
Engage stakeholders, including executives, and communicate technical concepts, project progress, and outcomes.
Guide clients through change processes related to data transformation and technology adoption.
Identify project risks and develop mitigation strategies.
Lead technical discussions and make architectural decisions.
Create documentation and reports on project progress and outcomes.
Stay current with trends and technologies in data engineering, cloud computing, and analytics.