Context Engineer - Manager - Consulting - Location OPEN

EY•Hartford, DC

1d•$125,500 - $261,600•Hybrid

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.