EY - GDS Consulting - AI and DATA - Data Engineer Lead - 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. 

 

 

 

 

Job Description for Lead Data Engineer

Rank – Manager

 

 

Objectives and Purpose

• The Lead Data Engineer leads large scale solution architecture design and optimisation to provide streamlined insights to partners throughout the business. This individual leads the team of Mid- and Senior data engineers to partner with visualization on data quality and troubleshooting needs.
• The Lead Data Engineer will:
  • Implement data processes for the data warehouse and internal systems
  • Lead a team of Junior and Senior Data Engineers in executing data processes and providing quality, timely data management
  • Managing data architecture, designing ETL process
  • Clean, aggregate and organize data from disparate sources and transfer it to data warehouses.
  • Lead development testing and maintenance of data pipelines and platforms, to enable data quality to be utilized within business dashboards and tools.
  • Support team members and direct reports in refining and validating data sets.
  • Create, maintain, and support the data platform and infrastructure that enables the analytics front-end; this includes the testing, maintenance, construction, and development of architectures such as high-volume, large-scale data processing and databases with proper verification and validation processes.

 

Your key responsibilities

Data Engineering

• Lead the design, development, optimization, and maintenance of data architecture and pipelines adhering to ETL principles and business goals.
• Develop and maintain scalable data pipelines, build out new integrations using AWS native technologies and data bricks to support increases in data source, volume, and complexity.
• Define data requirements, gather and mine large scale of structured and unstructured data, and validate data by running various data tools in the Big Data Environment.
• Lead the ad hoc data analysis, support standardization, customization and develop the mechanisms to ingest, analyze, validate, normalize, and clean data.
• Write unit/integration/performance test scripts and perform data analysis required to troubleshoot data related issues.
• Implement processes and systems to drive data reconciliation and monitor data quality, ensuring production data is always accurate and available for key stakeholders, downstream systems, and business processes.
• Lead the evaluation, implementation and deployment of emerging tools and processes for analytic data engineering to improve productivity.
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
• Solve complex data problems to deliver insights that help achieve business objectives.
• Partner with Business Analysts and Enterprise Architects to develop technical architectures for strategic enterprise projects and initiatives.
• Coordinate with Data Scientists, visualization developers and other data consumers to understand data requirements, and design solutions that enable advanced analytics, machine learning, and predictive modelling.

 

Relationship Building and Collaboration

• Partner with Business Analysts and Enterprise Architects to develop technical architectures for strategic enterprise projects and initiatives.
• Coordinate with Data Scientists, visualization developers and other data consumers to understand data requirements, and design solutions that enable advanced analytics, machine learning, and predictive modelling.
• Support Data Scientists in data sourcing and preparation to visualize data and synthesize insights of commercial value.
• Collaborate with AI/ML engineers to create data products for analytics and data scientist team members to improve productivity.
• Advise, consult, mentor and coach other data and analytic professionals on data standards and practices, promoting the values of learning and growth.
• Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.

 

Skills and attributes for success

Technical/Functional Expertise

• Advanced experience and understanding of data/Big Data, data integration, data modelling, AWS, and cloud technologies.
• Strong business acumen with knowledge of the Pharmaceutical, Healthcare, or Life Sciences sector is preferred, but not required.
• Expertise in building processes that support data transformation, workload management, data structures, dependency, and metadata.
• Expertise to build and optimize queries (SQL), data sets, 'Big Data' pipelines, and architectures for structured and unstructured data.
• Experience with or knowledge of Agile Software Development methodologies.

 

Leadership

• Mentoring Senior and Junior data engineers in the team
• Strategic mindset of thinking above the minor, tactical details and focusing on the long-term, strategic goals of the organization.
• Advocate of a culture of collaboration and psychological safety.

 

Decision-making and Autonomy

• Shift from manual decision-making to data-driven, strategic decision-making.
• Proven track record of applying critical thinking to resolve issues and overcome obstacles.

 

Interaction

• Proven track record of collaboration and developing strong working relationships with key stakeholders by building trust and being a true business partner.
• Demonstrated success in collaborating with different IT functions, contractors, and constituents to deliver data solutions that meet standards and security measures.

 

Innovation

• Passion for re-imagining new solutions, processes, and end-user experience by leveraging digital and disruptive technologies and developing advanced data and analytics solutions.
• Leading research and development (R&D) efforts in data engineering.
• Advocate of a culture of growth mindset, agility, and continuous improvement.

 

Complexity

• Demonstrates high multicultural sensitivity to lead teams effectively.
• Ability to coordinate and problem-solve amongst larger teams.

 

To qualify for the role, you must have the following:

Essential skillsets

• Bachelor’s degree in Engineering, Computer Science, Data Science, or related field
• 9+ years of experience in software development, data engineering, ETL, and analytics reporting development.
• Expert in building and maintaining data and system integrations using dimensional data modelling and optimized ETL pipelines.
• Advanced experience utilizing modern data architecture and frameworks like data mesh, data fabric, data product design
• Experience with designing data integration frameworks capable of supporting multiple data sources, consisting of both structured and unstructured data
• Proven track record of designing and implementing complex data solutions
• Demonstrated understanding and experience using:
  • Data Engineering Programming Languages (i.e., Python)
  • Distributed Data Technologies (e.g., Pyspark)
  • Cloud platform deployment and tools (e.g., Kubernetes)
  • Relational SQL databases
  • DevOps and continuous integration
  • AWS cloud services and technologies (i.e., Lambda, S3, DMS, Step Functions, Event Bridge, Cloud Watch, RDS)
  • Knowledge of data lakes, data warehouses, AI pipelines or similar
  • Databricks/ETL
  • IICS/DMS
  • GitHub
  • Event Bridge, Tidal
• Deep understanding of database architecture and administration
• Processes high proficiency in code programming languages (e.g., SQL, Python, Pyspark, AWS services) to design, maintain, and optimize data architecture/pipelines that fit business goals.
• Extracts, transforms, and loads data from multiple external/internal sources using Databricks Lakehouse/Data Lake concepts into a single, consistent source to serve business users and data visualization needs.
• Utilizes the principles of continuous integration and delivery to automate the deployment of code changes to elevate environments like by using GitHub Actions. 
• Excellent written and verbal communication skills, including storytelling and interacting effectively with multifunctional teams and other strategic partners.
• Strong organizational skills with the ability to manage multiple projects simultaneously, operating as leading member across globally distributed teams.
• Strong problem solving and troubleshooting skills.
• Ability to work in a fast-paced environment and adapt to changing business priorities.   
• Lead and oversee the code review process within the data engineering team to ensure high-quality, efficient, and maintainable code, ensure code is optimized for performance and scalability.
• Responsible for optimizing the performance of Python and Spark jobs/scripts to ensure efficient data processing.
• Identifying and implementing strategies to optimize AWS / Databricks cloud costs, ensuring efficient and cost-effective use of cloud resources.

 

Desired skillsets

• Master’s degree in Engineering, Computer Science, Data Science, or related field
• Experience in a global working environment

 

Travel requirements

• Access to transportation to attend meetings.
• Ability to fly to meetings regionally and globally.

 

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