EY delivers unparalleled tech consulting services in data strategy, business intelligence, digital, machine learning and Artificial Intelligence. We support and enable big ideas, always with the ambitions to keep doing more.

The opportunity

We are seeking a Data Engineer with strong semantic data engineering capabilities — someone who can design and build modern data pipelines while also implementing semantic frameworks (ontologies, taxonomies, and knowledge graphs) that enable agentic AI and intelligent data products.

In this role, you will work at the intersection of data engineering, knowledge representation, and AI enablement, helping create a context layer that allows AI systems to understand and reason over enterprise data. You will collaborate with data architects, AI engineers, and stakeholders to translate complex business concepts into scalable data + semantic models, powering downstream analytics, AI workflows, and operational use cases.

Your key responsibilities

In this role, you will contribute to multiple solutions and workstreams, focusing on delivery quality, operational excellence, and effective risk management. You will be proactive in continuous process improvement, leveraging research and analysis to identify innovative solutions.

Key responsibilities include:

Data engineering & platform delivery

• Design, build, and operate scalable data pipelines and integrations that support large-scale data architectures across cloud and hybrid environments.
• Build out new integrations using cloud-native technologies to support continuing increases in data sources, volume, and complexity.
• Extract, transform, and load data from multiple external/internal sources into a single, consistent source to serve business users and data visualization needs.
• Implement processes and systems to drive data reconciliation and monitor data quality.
• Write unit/integration/performance tests and perform analysis required to troubleshoot data-related issues and support resolution.

Semantic data engineering / context engineering

• Develop and evolve ontologies and semantic models to represent business concepts, relationships, and workflows in support of AI agents and enterprise analytics.
• Build and maintain knowledge graphs that enable contextual reasoning, entity resolution, and semantic integration across domains.
• Define metadata schemas, taxonomies, and contextual rules to support dynamic orchestration, discoverability, and reuse.
• Implement and govern semantic standards and best practices aligned to W3C semantic web standards

Continuous improvement

• Utilize CI/CD principles to automate deployment of code changes, improving code quality, test coverage, and resilience.
• Evaluate and adopt emerging tools and processes in data engineering and knowledge graph platforms to improve productivity and outcomes.

Skills and attributes for success

To thrive in this role, you will need a strong ability to manage change, lead teams effectively, and maintain excellent relationship management skills. You should be adept at problem-solving, critical thinking, and have a deep expertise in data architecture and software engineering.

• Partner with Business Analysts and Solution Architects to translate business requirements into technical specifications aligned to the intended design.
• Collaborate with AI/ML engineers to embed a semantic context layer into agentic workflows and data products.
• Support advanced analytics and AI use cases by improving data readiness, meaning, lineage, and interpretability.
• 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.
• Develop solutions for complex problems
• Suggest changes to policies and establish new procedures
• Provide direction and feedback to team members

To qualify for the role, you must have

• A Bachelor's degree in STEM
• 6+ years of relevant experience in software development, data science, data engineering, ETL, and analytics reporting development.
• 3+ years’ experience with native cloud products and services such as AWS, Azure or GCP.
• 3+ years of experience in the Life Science industry
• 2+ years of experience mentoring and leading a team of data engineers, fostering a culture of innovation and professional development.
• Experience designing, building, implementing, and maintaining data and system integrations using dimensional data modelling and development and optimization of ETL pipelines.
• Proven track record of designing and implementing complex data solutions.
• Demonstrated understanding and experience using:
• Data engineering programming languages (e.g., Python)
• Proficiency in OWL, RDF, SPARQL.
• Distributed data technologies (e.g., Pyspark)
• Cloud platform deployment and tools (e.g., Kubernetes)
• Relational SQL databases
• DevOps and continuous integration
• GitHub
• Strong organizational skills with the ability to manage multiple projects simultaneously and operate as a leading member across globally distributed teams to deliver high-quality services and solutions.
• Understanding of database architecture and administration.
• Excellent written and verbal communication skills, including storytelling and interacting effectively with multifunctional teams and other strategic partners.
• Strong problem solving and troubleshooting skills.
• Ability to work in a fast-paced environment and adapt to changing business priorities.

Ideally, you’ll also have

• Strong business acumen with the ability to manage complex client relationships
• Proven experience in managing and leading teams within a dynamic environment
• Excellent communication and interpersonal skills

• Experience with agentic AI systems or AI orchestration frameworks.
• Knowledge of data fabric, data mesh, or AI4Data architectures.
• Background in enterprise data management (MDM, metadata, governance).

• Experience with cloud data platforms like Snowflake and Databricks.
• Experience in leading and influencing teams, with a focus on mentorship and professional development.
• A passion for innovation and the strategic application of emerging technologies to solve real-world challenges.
• The ability to foster an inclusive environment that values diverse perspectives and empowers team members.

What we look for

We are looking for top performers who demonstrate a combination of technical expertise and leadership abilities. Ideal candidates are those who can navigate complex problems with innovative solutions, have a track record of successful project delivery, and possess the skills to build and maintain strong client relationships.

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