Databricks Data Engineer - Manager - Consulting - Location Open

EY•Hartford, DC

23d•$125,500 - $261,600•Hybrid

About The Position

We are looking for a dynamic and 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 will possess deep technical expertise in data architecture, cloud technologies, and analytics, along with exceptional leadership and client management skills. In this role, you will design and build analytics solutions that deliver significant business value. You will collaborate with other data and analytics professionals, management, and stakeholders to ensure that business requirements are translated into effective technical solutions. 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.

Requirements

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.
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.

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

Responsibilities

Leading workstream delivery and ensuring quality in all processes.
Engaging with clients on a daily basis, actively participating in working sessions, and identifying opportunities for additional services.
Implementing resource plans and budgets while managing engagement economics.
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.
Strategic Leadership: Ability to align data engineering initiatives with organizational goals and drive strategic vision.
Project Management: Experience in managing multiple projects and teams, ensuring timely delivery and adherence to project scope.
Stakeholder Engagement: Proficiency in engaging with various stakeholders, including executives, to understand their needs and present solutions effectively.
Change Management: Skills in guiding clients through change processes related to data transformation and technology adoption.
Risk Management: Ability to identify potential risks in data projects and develop mitigation strategies.
Technical Leadership: Experience in leading technical discussions and making architectural decisions that impact project outcomes.
Documentation and Reporting: Proficiency in creating comprehensive documentation and reports to communicate project progress and outcomes to clients.
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.